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Transfer learning and fine-tuning#
Copyright 2020 The TensorFlow Authors.
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In this tutorial, you will learn how to classify images of cats and dogs by using transfer learning from a pre-trained network.
A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. You either use the pretrained model as is or use transfer learning to customize this model to a given task.
The intuition behind transfer learning for image classification is that if a model is trained on a large and general enough dataset, this model will effectively serve as a generic model of the visual world. You can then take advantage of these learned feature maps without having to start from scratch by training a large model on a large dataset.
In this notebook, you will try two ways to customize a pretrained model:
Feature Extraction: Use the representations learned by a previous network to extract meaningful features from new samples. You simply add a new classifier, which will be trained from scratch, on top of the pretrained model so that you can repurpose the feature maps learned previously for the dataset.
You do not need to (re)train the entire model. The base convolutional network already contains features that are generically useful for classifying pictures. However, the final, classification part of the pretrained model is specific to the original classification task, and subsequently specific to the set of classes on which the model was trained.
Fine-Tuning: Unfreeze a few of the top layers of a frozen model base and jointly train both the newly-added classifier layers and the last layers of the base model. This allows us to “fine-tune” the higher-order feature representations in the base model in order to make them more relevant for the specific task.
You will follow the general machine learning workflow.
Examine and understand the data
Build an input pipeline, in this case using Keras ImageDataGenerator
Compose the model
Load in the pretrained base model (and pretrained weights)
Stack the classification layers on top
Train the model
Evaluate model
import os
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
Data preprocessing#
Data download#
In this tutorial, you will use a dataset containing several thousand images of cats and dogs. Download and extract a zip file containing the images, then create a tf.data.Dataset for training and validation using the tf.keras.utils.image_dataset_from_directory utility. You can learn more about loading images in this tutorial.
_URL = "https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip"
path_to_zip = tf.keras.utils.get_file("cats_and_dogs.zip", origin=_URL, extract=True)
PATH = os.path.join(os.path.dirname(path_to_zip), "cats_and_dogs_filtered")
train_dir = os.path.join(PATH, "train")
validation_dir = os.path.join(PATH, "validation")
BATCH_SIZE = 32
IMG_SIZE = (160, 160)
train_dataset = tf.keras.utils.image_dataset_from_directory(
train_dir, shuffle=True, batch_size=BATCH_SIZE, image_size=IMG_SIZE
)
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1723777686.391165 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777686.394572 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777686.398160 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777686.401884 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777686.413007 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777686.416056 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777686.419433 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777686.422845 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777686.426285 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777686.429299 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777686.432651 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777686.436148 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.681670 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.683734 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.685806 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.687890 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.689894 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.691778 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.693753 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.695758 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.697678 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.699566 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.701656 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.703653 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.742357 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.744341 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.746361 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.748387 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.750315 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.752217 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.754219 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.756268 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.758235 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.760671 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.763079 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723777687.765514 124422 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
validation_dataset = tf.keras.utils.image_dataset_from_directory(
validation_dir, shuffle=True, batch_size=BATCH_SIZE, image_size=IMG_SIZE
)
Show the first nine images and labels from the training set:
class_names = train_dataset.class_names
plt.figure(figsize=(10, 10))
for images, labels in train_dataset.take(1):
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(class_names[labels[i]])
plt.axis("off")
As the original dataset doesn’t contain a test set, you will create one. To do so, determine how many batches of data are available in the validation set using tf.data.experimental.cardinality, then move 20% of them to a test set.
val_batches = tf.data.experimental.cardinality(validation_dataset)
test_dataset = validation_dataset.take(val_batches // 5)
validation_dataset = validation_dataset.skip(val_batches // 5)
print(
"Number of validation batches: %d"
% tf.data.experimental.cardinality(validation_dataset)
)
print("Number of test batches: %d" % tf.data.experimental.cardinality(test_dataset))
Configure the dataset for performance#
Use buffered prefetching to load images from disk without having I/O become blocking. To learn more about this method see the data performance guide.
AUTOTUNE = tf.data.AUTOTUNE
train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)
validation_dataset = validation_dataset.prefetch(buffer_size=AUTOTUNE)
test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)
Use data augmentation#
When you don’t have a large image dataset, it’s a good practice to artificially introduce sample diversity by applying random, yet realistic, transformations to the training images, such as rotation and horizontal flipping. This helps expose the model to different aspects of the training data and reduce overfitting. You can learn more about data augmentation in this tutorial.
data_augmentation = tf.keras.Sequential(
[
tf.keras.layers.RandomFlip("horizontal"),
tf.keras.layers.RandomRotation(0.2),
]
)
Note: These layers are active only during training, when you call Model.fit. They are inactive when the model is used in inference mode in Model.evaluate, Model.predict, or Model.call.
Let’s repeatedly apply these layers to the same image and see the result.
for image, _ in train_dataset.take(1):
plt.figure(figsize=(10, 10))
first_image = image[0]
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
augmented_image = data_augmentation(tf.expand_dims(first_image, 0))
plt.imshow(augmented_image[0] / 255)
plt.axis("off")
Rescale pixel values#
In a moment, you will download tf.keras.applications.MobileNetV2 for use as your base model. This model expects pixel values in [-1, 1], but at this point, the pixel values in your images are in [0, 255]. To rescale them, use the preprocessing method included with the model.
preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input
Note: Alternatively, you could rescale pixel values from [0, 255] to [-1, 1] using tf.keras.layers.Rescaling.
rescale = tf.keras.layers.Rescaling(1.0 / 127.5, offset=-1)
Note: If using other tf.keras.applications, be sure to check the API doc to determine if they expect pixels in [-1, 1] or [0, 1], or use the included preprocess_input function.
Create the base model from the pre-trained convnets#
You will create the base model from the MobileNet V2 model developed at Google. This is pre-trained on the ImageNet dataset, a large dataset consisting of 1.4M images and 1000 classes. ImageNet is a research training dataset with a wide variety of categories like jackfruit and syringe. This base of knowledge will help us classify cats and dogs from our specific dataset.
First, you need to pick which layer of MobileNet V2 you will use for feature extraction. The very last classification layer (on “top”, as most diagrams of machine learning models go from bottom to top) is not very useful. Instead, you will follow the common practice to depend on the very last layer before the flatten operation. This layer is called the “bottleneck layer”. The bottleneck layer features retain more generality as compared to the final/top layer.
First, instantiate a MobileNet V2 model pre-loaded with weights trained on ImageNet. By specifying the include_top=False argument, you load a network that doesn’t include the classification layers at the top, which is ideal for feature extraction.
# Create the base model from the pre-trained model MobileNet V2
IMG_SHAPE = IMG_SIZE + (3,)
base_model = tf.keras.applications.MobileNetV2(
input_shape=IMG_SHAPE, include_top=False, weights="imagenet"
)
This feature extractor converts each 160x160x3 image into a 5x5x1280 block of features. Let’s see what it does to an example batch of images:
image_batch, label_batch = next(iter(train_dataset))
feature_batch = base_model(image_batch)
print(feature_batch.shape)
Feature extraction#
In this step, you will freeze the convolutional base created from the previous step and to use as a feature extractor. Additionally, you add a classifier on top of it and train the top-level classifier.
Freeze the convolutional base#
It is important to freeze the convolutional base before you compile and train the model. Freezing (by setting layer.trainable = False) prevents the weights in a given layer from being updated during training. MobileNet V2 has many layers, so setting the entire model’s trainable flag to False will freeze all of them.
base_model.trainable = False
Important note about BatchNormalization layers#
Many models contain tf.keras.layers.BatchNormalization layers. This layer is a special case and precautions should be taken in the context of fine-tuning, as shown later in this tutorial.
When you set layer.trainable = False, the BatchNormalization layer will run in inference mode, and will not update its mean and variance statistics.
When you unfreeze a model that contains BatchNormalization layers in order to do fine-tuning, you should keep the BatchNormalization layers in inference mode by passing training = False when calling the base model. Otherwise, the updates applied to the non-trainable weights will destroy what the model has learned.
For more details, see the Transfer learning guide.
# Let's take a look at the base model architecture
base_model.summary()
Add a classification head#
To generate predictions from the block of features, average over the spatial 5x5 spatial locations, using a tf.keras.layers.GlobalAveragePooling2D layer to convert the features to a single 1280-element vector per image.
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
feature_batch_average = global_average_layer(feature_batch)
print(feature_batch_average.shape)
Apply a tf.keras.layers.Dense layer to convert these features into a single prediction per image. You don’t need an activation function here because this prediction will be treated as a logit, or a raw prediction value. Positive numbers predict class 1, negative numbers predict class 0.
prediction_layer = tf.keras.layers.Dense(1, activation="sigmoid")
prediction_batch = prediction_layer(feature_batch_average)
print(prediction_batch.shape)
Build a model by chaining together the data augmentation, rescaling, base_model and feature extractor layers using the Keras Functional API. As previously mentioned, use training=False as our model contains a BatchNormalization layer.
inputs = tf.keras.Input(shape=(160, 160, 3))
x = data_augmentation(inputs)
x = preprocess_input(x)
x = base_model(x, training=False)
x = global_average_layer(x)
x = tf.keras.layers.Dropout(0.2)(x)
outputs = prediction_layer(x)
model = tf.keras.Model(inputs, outputs)
model.summary()
The 8+ million parameters in MobileNet are frozen, but there are 1.2 thousand trainable parameters in the Dense layer. These are divided between two tf.Variable objects, the weights and biases.
len(model.trainable_variables)
tf.keras.utils.plot_model(model, show_shapes=True)
Compile the model#
Compile the model before training it. Since there are two classes and a sigmoid oputput, use the BinaryAccuracy.
base_learning_rate = 0.0001
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0.5, name="accuracy")],
)
Train the model#
After training for 10 epochs, you should see ~96% accuracy on the validation set.
initial_epochs = 10
loss0, accuracy0 = model.evaluate(validation_dataset)
W0000 00:00:1723777697.582166 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.582939 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.589370 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.590063 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.590736 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.591407 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.592071 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.592733 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.593421 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.594107 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.594789 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.595481 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.596156 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.596862 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.597564 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.598264 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.598968 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.599741 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.605659 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.606345 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.607015 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.607691 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.608358 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.609024 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.609685 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.610357 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.611042 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.611719 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.612386 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.613058 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.613739 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.614483 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.615179 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.615893 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.622128 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.622849 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.623524 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.624191 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.624878 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.625562 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.626286 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.626989 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.627701 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.628422 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.629139 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.629870 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.630610 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.631397 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.632127 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.638324 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.639038 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.639707 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.640379 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.641084 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.641780 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.642499 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.643212 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.643929 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.644657 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.645385 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.646068 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.646771 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.647508 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.648286 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.654355 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.655059 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.655722 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.656386 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.657055 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.657733 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.658407 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.659079 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.659761 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.660451 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.661139 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.661803 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.662481 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.663138 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.663794 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.664519 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.670260 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.670986 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.671685 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.672377 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.673122 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.673863 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.674647 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.675419 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.676186 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.676987 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.677782 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.678501 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.679220 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.680000 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.680785 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.686753 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.687496 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.688201 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.688895 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.689616 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.690343 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.691118 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.691849 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.692579 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.693346 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.694115 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.695055 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.695860 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.696658 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.697463 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.704422 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.705160 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.705852 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.706573 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.707264 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.707949 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.708636 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.709343 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.710047 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.710758 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.711456 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.712155 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.712848 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.713545 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.714244 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777697.715044 124619 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
print("initial loss: {:.2f}".format(loss0))
print("initial accuracy: {:.2f}".format(accuracy0))
history = model.fit(
train_dataset, epochs=initial_epochs, validation_data=validation_dataset
)
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.5905 - loss: 0.7091
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.5919 - loss: 0.7077
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.5932 - loss: 0.7063
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.5945 - loss: 0.7048
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.5959 - loss: 0.7032
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.5972 - loss: 0.7018
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.5985 - loss: 0.7003
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.5997 - loss: 0.6990
W0000 00:00:1723777704.397737 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.398671 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.399497 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.400322 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.401154 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.402015 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.402938 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.403723 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.404513 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.405339 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.406274 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.407173 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.408044 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.408940 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.411034 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.417910 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.418727 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.419494 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.420256 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.421017 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.421806 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.422581 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.423379 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.424140 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.424899 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.425677 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.426514 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.427492 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.428386 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.429216 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.430281 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.431136 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.433650 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.440239 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.441208 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.442150 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.443083 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.444007 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.444886 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.445789 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.446665 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.447540 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.448800 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.450017 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.451480 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.452586 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.453662 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.454795 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.455941 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.463035 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.463784 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.464489 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.465189 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.465892 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.466643 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.467370 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.468107 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.468826 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.469544 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.470264 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.470995 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.471820 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.472561 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.473375 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.474369 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.475156 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.476418 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.483064 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.483859 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.484625 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.485401 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.486168 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.486927 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.487713 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.488449 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.489174 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.490068 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.490952 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.491923 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.492753 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.493561 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.494392 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.495227 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.496281 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.503437 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.504189 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.504908 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.505623 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.506347 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.507076 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.507815 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.508574 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.509302 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.510028 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.510771 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.511510 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.512403 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.513177 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.514077 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.515176 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.516001 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.517532 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.524799 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.525491 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.526168 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.526827 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.527502 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.528174 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.528850 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.529517 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.530184 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.530857 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.531529 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.532202 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.532929 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.533622 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.534316 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.535080 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.535851 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.536920 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.543260 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.543963 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.544627 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.545306 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.545989 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.546747 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.547439 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.548127 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.548830 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.549503 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.550176 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.550886 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.551554 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.552265 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.553250 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.553933 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.554716 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.561027 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.561728 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.562408 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.563075 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.563753 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.564416 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.565094 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.565779 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.566463 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.567187 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.567875 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.568564 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.569291 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.569996 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.570707 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.571499 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.572308 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.573558 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.580191 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.580883 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.581555 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.582196 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.582852 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.583522 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.584192 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.584853 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.585525 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.586189 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.586862 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.587554 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.588235 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.588915 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.589585 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.590335 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.596016 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.596725 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.597434 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.598138 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.598822 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 36ms/step - accuracy: 0.6008 - loss: 0.6978
W0000 00:00:1723777704.599494 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.600166 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.600837 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.601515 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.602177 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.602866 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.603530 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.604222 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.604885 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.611237 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.611961 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.612669 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.613356 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.614060 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.614765 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.615453 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.616118 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.616787 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.617496 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.618172 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.618837 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.619546 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.620255 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.620971 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.621756 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.628424 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.629148 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.629832 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.630533 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.631233 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.631920 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.632604 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.633280 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.633948 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.634649 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.635341 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.636029 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.636730 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.637437 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.638155 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.638966 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.645275 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.645984 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.646701 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.647391 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.648129 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.648798 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.649458 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.650152 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.650851 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.651542 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.652234 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.652940 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.653657 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.654351 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.655049 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.655790 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.662700 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.663465 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.664181 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.664915 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.665649 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.666373 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.667076 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.667785 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.668477 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.669208 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.669920 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.670640 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.671370 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.672110 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.672850 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.673679 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.680300 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.681010 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.681692 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.682358 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.683054 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.683749 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.684467 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.685181 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.685899 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.686634 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.687367 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.688100 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.688826 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.689555 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.690340 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.696654 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.697377 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.698075 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.698792 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.699486 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.700170 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.700855 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.701527 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.702216 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.702882 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.703576 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.704258 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.704954 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.705612 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.706309 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.707057 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.712992 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.713730 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.714434 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.715138 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.715883 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.716624 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.717425 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.718222 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.718994 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.719799 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.720597 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.721396 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.722170 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.722964 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.723771 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.731009 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.731774 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.732515 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.733246 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.734034 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.734825 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.735600 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.736317 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.737107 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.737911 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.738709 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.739497 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.740221 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.741014 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.741807 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.742667 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.750284 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.751033 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.751759 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.752497 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.753195 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.753972 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.754762 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.755481 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.756211 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.756946 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.757670 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.758401 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.759143 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.759895 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.760646 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
W0000 00:00:1723777704.761504 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
63/63 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6013 - loss: 0.6973 - val_accuracy: 0.8292 - val_loss: 0.4765
Epoch 2/10
1/63 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - accuracy: 0.5625 - loss: 0.6844
3/63 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - accuracy: 0.5764 - loss: 0.7090
5/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6243 - loss: 0.6747
7/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6466 - loss: 0.6556
9/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6565 - loss: 0.6434
11/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6626 - loss: 0.6359
13/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6684 - loss: 0.6293
15/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6735 - loss: 0.6235
17/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6778 - loss: 0.6178
19/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6815 - loss: 0.6123
21/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6845 - loss: 0.6075
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6874 - loss: 0.6027
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6905 - loss: 0.5979
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.6933 - loss: 0.5936
29/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.6959 - loss: 0.5898
31/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.6984 - loss: 0.5865
33/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7007 - loss: 0.5833
35/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7030 - loss: 0.5803
37/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7052 - loss: 0.5774
39/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7071 - loss: 0.5748
41/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7089 - loss: 0.5724
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7106 - loss: 0.5702
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7122 - loss: 0.5681
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7138 - loss: 0.5661
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7152 - loss: 0.5642
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7165 - loss: 0.5624
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7177 - loss: 0.5607
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7189 - loss: 0.5591
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7201 - loss: 0.5575
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7212 - loss: 0.5560
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7221 - loss: 0.5546
63/63 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step - accuracy: 0.7236 - loss: 0.5526 - val_accuracy: 0.9047 - val_loss: 0.3534
Epoch 3/10
1/63 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - accuracy: 0.7500 - loss: 0.4450
3/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7604 - loss: 0.4616
5/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7663 - loss: 0.4636
7/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7692 - loss: 0.4634
9/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7739 - loss: 0.4611
11/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7761 - loss: 0.4616
13/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7772 - loss: 0.4624
15/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7778 - loss: 0.4624
17/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7779 - loss: 0.4620
19/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7780 - loss: 0.4616
21/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7785 - loss: 0.4612
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7793 - loss: 0.4603
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7801 - loss: 0.4593
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.7806 - loss: 0.4585
29/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7814 - loss: 0.4573
31/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7819 - loss: 0.4564
33/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7823 - loss: 0.4556
35/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7828 - loss: 0.4549
37/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7832 - loss: 0.4543
39/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7836 - loss: 0.4537
41/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7840 - loss: 0.4531
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7844 - loss: 0.4526
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7848 - loss: 0.4521
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7853 - loss: 0.4516
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7858 - loss: 0.4511
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7864 - loss: 0.4504
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7870 - loss: 0.4497
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7876 - loss: 0.4491
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7883 - loss: 0.4485
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7888 - loss: 0.4478
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7894 - loss: 0.4472
63/63 ━━━━━━━━━━━━━━━━━━━━ 3s 40ms/step - accuracy: 0.7903 - loss: 0.4462 - val_accuracy: 0.9282 - val_loss: 0.2795
Epoch 4/10
1/63 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - accuracy: 0.8125 - loss: 0.3939
3/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8403 - loss: 0.3769
5/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8523 - loss: 0.3685
7/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8546 - loss: 0.3655
9/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8576 - loss: 0.3630
11/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8586 - loss: 0.3606
13/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8590 - loss: 0.3587
15/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8587 - loss: 0.3582
17/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8584 - loss: 0.3579
19/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8576 - loss: 0.3581
21/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8577 - loss: 0.3580
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8577 - loss: 0.3579
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8579 - loss: 0.3578
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8582 - loss: 0.3579
29/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8581 - loss: 0.3582
31/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8581 - loss: 0.3584
33/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8579 - loss: 0.3585
35/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8576 - loss: 0.3587
37/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8574 - loss: 0.3588
39/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8573 - loss: 0.3587
41/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8572 - loss: 0.3587
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8572 - loss: 0.3588
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8569 - loss: 0.3590
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8566 - loss: 0.3591
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8564 - loss: 0.3592
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8562 - loss: 0.3592
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8562 - loss: 0.3591
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8563 - loss: 0.3589
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8563 - loss: 0.3587
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8563 - loss: 0.3585
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8563 - loss: 0.3585
63/63 ━━━━━━━━━━━━━━━━━━━━ 3s 40ms/step - accuracy: 0.8562 - loss: 0.3583 - val_accuracy: 0.9443 - val_loss: 0.2279
Epoch 5/10
1/63 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - accuracy: 0.9062 - loss: 0.3328
3/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9097 - loss: 0.3131
5/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9124 - loss: 0.3047
7/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9052 - loss: 0.3056
9/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8997 - loss: 0.3112
11/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8960 - loss: 0.3150
13/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8939 - loss: 0.3175
15/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8930 - loss: 0.3186
17/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8917 - loss: 0.3189
19/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8909 - loss: 0.3184
21/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8910 - loss: 0.3174
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8910 - loss: 0.3169
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8911 - loss: 0.3162
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8911 - loss: 0.3159
29/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8912 - loss: 0.3155
31/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8913 - loss: 0.3148
33/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8916 - loss: 0.3143
35/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8917 - loss: 0.3140
37/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8917 - loss: 0.3139
39/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8917 - loss: 0.3138
41/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8917 - loss: 0.3136
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8918 - loss: 0.3135
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8918 - loss: 0.3133
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8917 - loss: 0.3132
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8915 - loss: 0.3131
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8913 - loss: 0.3131
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8911 - loss: 0.3131
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8909 - loss: 0.3130
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8907 - loss: 0.3128
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8906 - loss: 0.3127
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8904 - loss: 0.3128
63/63 ━━━━━━━━━━━━━━━━━━━━ 3s 40ms/step - accuracy: 0.8901 - loss: 0.3128 - val_accuracy: 0.9554 - val_loss: 0.1954
Epoch 6/10
1/63 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - accuracy: 0.9375 - loss: 0.2704
3/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8889 - loss: 0.3159
5/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8805 - loss: 0.3255
7/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8699 - loss: 0.3332
9/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8653 - loss: 0.3360
11/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8633 - loss: 0.3365
13/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8628 - loss: 0.3352
15/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8637 - loss: 0.3324
17/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8653 - loss: 0.3291
19/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8670 - loss: 0.3260
21/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8685 - loss: 0.3233
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8698 - loss: 0.3211
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8707 - loss: 0.3190
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8716 - loss: 0.3170
29/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8727 - loss: 0.3149
31/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8737 - loss: 0.3130
33/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8747 - loss: 0.3112
35/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8758 - loss: 0.3095
37/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8768 - loss: 0.3078
39/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8779 - loss: 0.3062
41/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8790 - loss: 0.3046
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8801 - loss: 0.3031
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8810 - loss: 0.3019
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8818 - loss: 0.3007
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8825 - loss: 0.2997
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8832 - loss: 0.2988
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8837 - loss: 0.2980
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8842 - loss: 0.2972
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8847 - loss: 0.2964
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8852 - loss: 0.2956
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8856 - loss: 0.2949
63/63 ━━━━━━━━━━━━━━━━━━━━ 3s 40ms/step - accuracy: 0.8862 - loss: 0.2941 - val_accuracy: 0.9567 - val_loss: 0.1724
Epoch 7/10
1/63 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - accuracy: 0.9062 - loss: 0.3483
3/63 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - accuracy: 0.9306 - loss: 0.2985
5/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9265 - loss: 0.2989
7/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9228 - loss: 0.2965
9/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9203 - loss: 0.2938
11/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9195 - loss: 0.2905
13/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9170 - loss: 0.2895
15/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9159 - loss: 0.2881
17/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9156 - loss: 0.2859
19/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9157 - loss: 0.2835
21/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9158 - loss: 0.2812
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9157 - loss: 0.2793
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9153 - loss: 0.2781
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9150 - loss: 0.2769
29/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9149 - loss: 0.2755
31/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9150 - loss: 0.2739
33/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9149 - loss: 0.2728
35/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9145 - loss: 0.2723
37/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9141 - loss: 0.2718
39/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9138 - loss: 0.2714
41/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9136 - loss: 0.2709
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9133 - loss: 0.2704
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9131 - loss: 0.2700
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9130 - loss: 0.2696
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9127 - loss: 0.2693
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9123 - loss: 0.2691
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9121 - loss: 0.2689
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9118 - loss: 0.2687
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9116 - loss: 0.2685
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9114 - loss: 0.2682
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9111 - loss: 0.2681
63/63 ━━━━━━━━━━━━━━━━━━━━ 3s 40ms/step - accuracy: 0.9106 - loss: 0.2680 - val_accuracy: 0.9604 - val_loss: 0.1556
Epoch 8/10
1/63 ━━━━━━━━━━━━━━━━━━━━ 3s 50ms/step - accuracy: 0.8750 - loss: 0.2662
3/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8698 - loss: 0.2745
5/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8759 - loss: 0.2721
7/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8805 - loss: 0.2695
9/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8825 - loss: 0.2680
11/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8830 - loss: 0.2676
13/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8839 - loss: 0.2674
15/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8854 - loss: 0.2662
17/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8875 - loss: 0.2641
19/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8899 - loss: 0.2618
21/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8920 - loss: 0.2594
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8939 - loss: 0.2575
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8959 - loss: 0.2556
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.8978 - loss: 0.2537
29/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.8994 - loss: 0.2520
31/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9009 - loss: 0.2506
33/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9023 - loss: 0.2493
35/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9033 - loss: 0.2483
37/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9039 - loss: 0.2479
39/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9043 - loss: 0.2477
41/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9046 - loss: 0.2475
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9047 - loss: 0.2476
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9048 - loss: 0.2476
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9047 - loss: 0.2478
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9047 - loss: 0.2481
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9047 - loss: 0.2484
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9048 - loss: 0.2487
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9048 - loss: 0.2489
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9048 - loss: 0.2490
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9049 - loss: 0.2490
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9049 - loss: 0.2491
63/63 ━━━━━━━━━━━━━━━━━━━━ 3s 40ms/step - accuracy: 0.9050 - loss: 0.2492 - val_accuracy: 0.9604 - val_loss: 0.1435
Epoch 9/10
1/63 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - accuracy: 0.9375 - loss: 0.1943
3/63 ━━━━━━━━━━━━━━━━━━━━ 1s 30ms/step - accuracy: 0.9201 - loss: 0.2273
5/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9230 - loss: 0.2254
7/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9258 - loss: 0.2193
9/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9268 - loss: 0.2178
11/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9271 - loss: 0.2172
13/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9276 - loss: 0.2160
15/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9275 - loss: 0.2146
17/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9273 - loss: 0.2138
19/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9271 - loss: 0.2132
21/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9266 - loss: 0.2135
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9262 - loss: 0.2142
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9259 - loss: 0.2147
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9257 - loss: 0.2150
29/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9256 - loss: 0.2153
31/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9254 - loss: 0.2157
33/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9251 - loss: 0.2161
35/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9247 - loss: 0.2166
37/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9244 - loss: 0.2170
39/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9242 - loss: 0.2172
41/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9240 - loss: 0.2175
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9239 - loss: 0.2177
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9236 - loss: 0.2181
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9233 - loss: 0.2185
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9230 - loss: 0.2190
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9227 - loss: 0.2194
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9223 - loss: 0.2199
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9220 - loss: 0.2203
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9218 - loss: 0.2206
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9216 - loss: 0.2209
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9214 - loss: 0.2212
63/63 ━━━━━━━━━━━━━━━━━━━━ 3s 40ms/step - accuracy: 0.9211 - loss: 0.2216 - val_accuracy: 0.9641 - val_loss: 0.1348
Epoch 10/10
1/63 ━━━━━━━━━━━━━━━━━━━━ 3s 49ms/step - accuracy: 0.9688 - loss: 0.2103
3/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9358 - loss: 0.2372
5/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9349 - loss: 0.2312
7/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9322 - loss: 0.2284
9/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9318 - loss: 0.2240
11/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9312 - loss: 0.2210
13/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9303 - loss: 0.2193
15/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9296 - loss: 0.2181
17/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9294 - loss: 0.2168
19/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9293 - loss: 0.2157
21/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9294 - loss: 0.2147
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9293 - loss: 0.2143
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9292 - loss: 0.2142
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9290 - loss: 0.2143
29/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9288 - loss: 0.2145
31/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9287 - loss: 0.2146
33/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9285 - loss: 0.2146
35/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9285 - loss: 0.2145
37/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9283 - loss: 0.2145
39/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9282 - loss: 0.2146
41/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9280 - loss: 0.2149
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9278 - loss: 0.2150
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9276 - loss: 0.2151
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9274 - loss: 0.2154
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9272 - loss: 0.2157
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9269 - loss: 0.2159
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9267 - loss: 0.2161
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9265 - loss: 0.2163
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9263 - loss: 0.2165
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9260 - loss: 0.2168
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9258 - loss: 0.2170
63/63 ━━━━━━━━━━━━━━━━━━━━ 3s 41ms/step - accuracy: 0.9256 - loss: 0.2171 - val_accuracy: 0.9653 - val_loss: 0.1308
Learning curves#
Let’s take a look at the learning curves of the training and validation accuracy/loss when using the MobileNetV2 base model as a fixed feature extractor.
acc = history.history["accuracy"]
val_acc = history.history["val_accuracy"]
loss = history.history["loss"]
val_loss = history.history["val_loss"]
plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label="Training Accuracy")
plt.plot(val_acc, label="Validation Accuracy")
plt.legend(loc="lower right")
plt.ylabel("Accuracy")
plt.ylim([min(plt.ylim()), 1])
plt.title("Training and Validation Accuracy")
plt.subplot(2, 1, 2)
plt.plot(loss, label="Training Loss")
plt.plot(val_loss, label="Validation Loss")
plt.legend(loc="upper right")
plt.ylabel("Cross Entropy")
plt.ylim([0, 1.0])
plt.title("Training and Validation Loss")
plt.xlabel("epoch")
plt.show()
Note: If you are wondering why the validation metrics are clearly better than the training metrics, the main factor is because layers like tf.keras.layers.BatchNormalization and tf.keras.layers.Dropout affect accuracy during training. They are turned off when calculating validation loss.
To a lesser extent, it is also because training metrics report the average for an epoch, while validation metrics are evaluated after the epoch, so validation metrics see a model that has trained slightly longer.
Fine tuning#
In the feature extraction experiment, you were only training a few layers on top of an MobileNetV2 base model. The weights of the pre-trained network were not updated during training.
One way to increase performance even further is to train (or “fine-tune”) the weights of the top layers of the pre-trained model alongside the training of the classifier you added. The training process will force the weights to be tuned from generic feature maps to features associated specifically with the dataset.
Note: This should only be attempted after you have trained the top-level classifier with the pre-trained model set to non-trainable. If you add a randomly initialized classifier on top of a pre-trained model and attempt to train all layers jointly, the magnitude of the gradient updates will be too large (due to the random weights from the classifier) and your pre-trained model will forget what it has learned.
Also, you should try to fine-tune a small number of top layers rather than the whole MobileNet model. In most convolutional networks, the higher up a layer is, the more specialized it is. The first few layers learn very simple and generic features that generalize to almost all types of images. As you go higher up, the features are increasingly more specific to the dataset on which the model was trained. The goal of fine-tuning is to adapt these specialized features to work with the new dataset, rather than overwrite the generic learning.
Un-freeze the top layers of the model#
All you need to do is unfreeze the base_model and set the bottom layers to be un-trainable. Then, you should recompile the model (necessary for these changes to take effect), and resume training.
base_model.trainable = True
# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))
# Fine-tune from this layer onwards
fine_tune_at = 100
# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at]:
layer.trainable = False
Compile the model#
As you are training a much larger model and want to readapt the pretrained weights, it is important to use a lower learning rate at this stage. Otherwise, your model could overfit very quickly.
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(),
optimizer=tf.keras.optimizers.RMSprop(learning_rate=base_learning_rate / 10),
metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0.5, name="accuracy")],
)
model.summary()
len(model.trainable_variables)
Continue training the model#
If you trained to convergence earlier, this step will improve your accuracy by a few percentage points.
fine_tune_epochs = 10
total_epochs = initial_epochs + fine_tune_epochs
history_fine = model.fit(
train_dataset,
epochs=total_epochs,
initial_epoch=len(history.epoch),
validation_data=validation_dataset,
)
31/63 ━━━━━━━━━━━━━━━━━━━━ 1s 48ms/step - accuracy: 0.7750 - loss: 0.4563
32/63 ━━━━━━━━━━━━━━━━━━━━ 1s 48ms/step - accuracy: 0.7750 - loss: 0.4558
34/63 ━━━━━━━━━━━━━━━━━━━━ 1s 48ms/step - accuracy: 0.7753 - loss: 0.4547
36/63 ━━━━━━━━━━━━━━━━━━━━ 1s 48ms/step - accuracy: 0.7755 - loss: 0.4538
38/63 ━━━━━━━━━━━━━━━━━━━━ 1s 48ms/step - accuracy: 0.7757 - loss: 0.4529
40/63 ━━━━━━━━━━━━━━━━━━━━ 1s 48ms/step - accuracy: 0.7761 - loss: 0.4518
42/63 ━━━━━━━━━━━━━━━━━━━━ 1s 48ms/step - accuracy: 0.7766 - loss: 0.4507
44/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7770 - loss: 0.4497
46/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7775 - loss: 0.4486
48/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7780 - loss: 0.4475
50/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7786 - loss: 0.4464
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7789 - loss: 0.4459
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7795 - loss: 0.4448
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7801 - loss: 0.4436
56/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7804 - loss: 0.4431
58/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7810 - loss: 0.4420
60/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7818 - loss: 0.4408
62/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.7825 - loss: 0.4395
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.7829 - loss: 0.4388
W0000 00:00:1723777740.759928 124620 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
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63/63 ━━━━━━━━━━━━━━━━━━━━ 14s 79ms/step - accuracy: 0.7833 - loss: 0.4382 - val_accuracy: 0.9740 - val_loss: 0.0901
Epoch 12/20
1/63 ━━━━━━━━━━━━━━━━━━━━ 4s 66ms/step - accuracy: 0.8750 - loss: 0.2900
3/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.8819 - loss: 0.2776
5/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.8860 - loss: 0.2764
7/63 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - accuracy: 0.8862 - loss: 0.2743
8/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.8872 - loss: 0.2727
10/63 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - accuracy: 0.8858 - loss: 0.2743
12/63 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - accuracy: 0.8847 - loss: 0.2745
13/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.8845 - loss: 0.2745
15/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.8838 - loss: 0.2754
17/63 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - accuracy: 0.8832 - loss: 0.2763
18/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.8831 - loss: 0.2766
20/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.8832 - loss: 0.2765
22/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8836 - loss: 0.2763
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8838 - loss: 0.2762
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8840 - loss: 0.2762
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8841 - loss: 0.2763
28/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8842 - loss: 0.2762
30/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8845 - loss: 0.2759
32/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8846 - loss: 0.2759
34/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8843 - loss: 0.2764
36/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8842 - loss: 0.2765
38/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8840 - loss: 0.2767
40/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8839 - loss: 0.2767
42/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.8839 - loss: 0.2765
44/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.8840 - loss: 0.2763
46/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.8841 - loss: 0.2761
48/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.8842 - loss: 0.2757
50/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.8844 - loss: 0.2754
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.8845 - loss: 0.2753
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.8847 - loss: 0.2749
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.8849 - loss: 0.2747
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.8850 - loss: 0.2743
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.8851 - loss: 0.2740
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.8852 - loss: 0.2737
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.8853 - loss: 0.2735
63/63 ━━━━━━━━━━━━━━━━━━━━ 4s 60ms/step - accuracy: 0.8853 - loss: 0.2733 - val_accuracy: 0.9715 - val_loss: 0.0812
Epoch 13/20
1/63 ━━━━━━━━━━━━━━━━━━━━ 4s 65ms/step - accuracy: 0.9062 - loss: 0.2526
3/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9427 - loss: 0.2120
5/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9519 - loss: 0.1942
7/63 ━━━━━━━━━━━━━━━━━━━━ 2s 48ms/step - accuracy: 0.9485 - loss: 0.1909
9/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9445 - loss: 0.1894
11/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9387 - loss: 0.1916
13/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9344 - loss: 0.1945
14/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9327 - loss: 0.1958
16/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9308 - loss: 0.1974
18/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9289 - loss: 0.1994
19/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9280 - loss: 0.2005
21/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9265 - loss: 0.2023
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9253 - loss: 0.2038
24/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9248 - loss: 0.2044
26/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9238 - loss: 0.2055
28/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9229 - loss: 0.2065
30/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9221 - loss: 0.2073
31/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9218 - loss: 0.2076
33/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9213 - loss: 0.2081
35/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9207 - loss: 0.2089
36/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9203 - loss: 0.2093
38/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9198 - loss: 0.2102
40/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9192 - loss: 0.2110
41/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9188 - loss: 0.2114
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9183 - loss: 0.2121
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9178 - loss: 0.2127
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9175 - loss: 0.2133
48/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9172 - loss: 0.2136
50/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9169 - loss: 0.2141
52/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9166 - loss: 0.2146
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9165 - loss: 0.2149
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9162 - loss: 0.2154
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9160 - loss: 0.2158
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9158 - loss: 0.2161
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9158 - loss: 0.2163
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step - accuracy: 0.9156 - loss: 0.2165
63/63 ━━━━━━━━━━━━━━━━━━━━ 4s 60ms/step - accuracy: 0.9156 - loss: 0.2167 - val_accuracy: 0.9777 - val_loss: 0.0656
Epoch 14/20
1/63 ━━━━━━━━━━━━━━━━━━━━ 4s 66ms/step - accuracy: 1.0000 - loss: 0.0997
2/63 ━━━━━━━━━━━━━━━━━━━━ 3s 50ms/step - accuracy: 0.9766 - loss: 0.1141
4/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9681 - loss: 0.1321
5/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9657 - loss: 0.1380
7/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9584 - loss: 0.1468
9/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9510 - loss: 0.1533
11/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9463 - loss: 0.1575
12/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9450 - loss: 0.1587
14/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9434 - loss: 0.1597
15/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9427 - loss: 0.1605
17/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9419 - loss: 0.1613
19/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9412 - loss: 0.1623
21/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9408 - loss: 0.1632
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9403 - loss: 0.1641
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9397 - loss: 0.1650
26/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9394 - loss: 0.1654
28/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9390 - loss: 0.1662
30/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9388 - loss: 0.1667
31/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9387 - loss: 0.1669
33/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9384 - loss: 0.1676
34/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9382 - loss: 0.1679
36/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9379 - loss: 0.1687
38/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9376 - loss: 0.1694
40/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9374 - loss: 0.1702
42/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9371 - loss: 0.1709
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9370 - loss: 0.1713
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9368 - loss: 0.1720
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9366 - loss: 0.1725
48/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9365 - loss: 0.1728
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9364 - loss: 0.1730
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9363 - loss: 0.1733
52/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9363 - loss: 0.1735
54/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9362 - loss: 0.1738
56/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9361 - loss: 0.1741
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9360 - loss: 0.1742
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9359 - loss: 0.1746
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9357 - loss: 0.1749
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9356 - loss: 0.1752
63/63 ━━━━━━━━━━━━━━━━━━━━ 4s 60ms/step - accuracy: 0.9355 - loss: 0.1753 - val_accuracy: 0.9790 - val_loss: 0.0592
Epoch 15/20
1/63 ━━━━━━━━━━━━━━━━━━━━ 4s 65ms/step - accuracy: 0.9688 - loss: 0.1888
3/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9514 - loss: 0.1675
5/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9483 - loss: 0.1590
6/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9491 - loss: 0.1550
7/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9507 - loss: 0.1513
9/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9514 - loss: 0.1477
10/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9513 - loss: 0.1464
12/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9513 - loss: 0.1447
14/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9493 - loss: 0.1469
15/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9483 - loss: 0.1485
17/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9459 - loss: 0.1516
19/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9439 - loss: 0.1537
21/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9426 - loss: 0.1551
22/63 ━━━━━━━━━━━━━━━━━━━━ 2s 49ms/step - accuracy: 0.9422 - loss: 0.1554
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9417 - loss: 0.1559
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9409 - loss: 0.1567
26/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9407 - loss: 0.1570
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9404 - loss: 0.1575
29/63 ━━━━━━━━━━━━━━━━━━━━ 1s 49ms/step - accuracy: 0.9400 - loss: 0.1583
30/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9398 - loss: 0.1586
32/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9394 - loss: 0.1591
33/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9392 - loss: 0.1593
34/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9391 - loss: 0.1596
36/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9388 - loss: 0.1603
37/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9386 - loss: 0.1606
38/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9385 - loss: 0.1610
40/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9382 - loss: 0.1617
41/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9381 - loss: 0.1620
43/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9380 - loss: 0.1625
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9379 - loss: 0.1628
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9377 - loss: 0.1632
48/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9377 - loss: 0.1634
50/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9376 - loss: 0.1637
52/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9374 - loss: 0.1640
54/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9372 - loss: 0.1644
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9371 - loss: 0.1646
56/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9370 - loss: 0.1648
58/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9368 - loss: 0.1651
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9366 - loss: 0.1652
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9364 - loss: 0.1655
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 49ms/step - accuracy: 0.9363 - loss: 0.1657
63/63 ━━━━━━━━━━━━━━━━━━━━ 4s 61ms/step - accuracy: 0.9362 - loss: 0.1658 - val_accuracy: 0.9802 - val_loss: 0.0545
Epoch 16/20
1/63 ━━━━━━━━━━━━━━━━━━━━ 4s 67ms/step - accuracy: 0.9688 - loss: 0.1078
2/63 ━━━━━━━━━━━━━━━━━━━━ 3s 52ms/step - accuracy: 0.9609 - loss: 0.1243
4/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9434 - loss: 0.1378
5/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9384 - loss: 0.1418
6/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9374 - loss: 0.1417
8/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9364 - loss: 0.1415
9/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9357 - loss: 0.1426
11/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9350 - loss: 0.1451
12/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9350 - loss: 0.1461
13/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9354 - loss: 0.1464
15/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9359 - loss: 0.1468
16/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9364 - loss: 0.1466
17/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9369 - loss: 0.1462
19/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9379 - loss: 0.1454
20/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9381 - loss: 0.1454
21/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9381 - loss: 0.1455
23/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9385 - loss: 0.1453
24/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9385 - loss: 0.1454
26/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9387 - loss: 0.1455
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9387 - loss: 0.1455
28/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9387 - loss: 0.1455
30/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9387 - loss: 0.1455
32/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9387 - loss: 0.1455
33/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9388 - loss: 0.1454
34/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9388 - loss: 0.1455
35/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9388 - loss: 0.1455
36/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9388 - loss: 0.1455
38/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9390 - loss: 0.1454
39/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9391 - loss: 0.1454
40/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9391 - loss: 0.1454
42/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9392 - loss: 0.1454
43/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9392 - loss: 0.1454
44/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9392 - loss: 0.1455
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9392 - loss: 0.1456
46/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9391 - loss: 0.1456
48/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9391 - loss: 0.1457
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9391 - loss: 0.1458
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9392 - loss: 0.1458
52/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9392 - loss: 0.1458
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9392 - loss: 0.1458
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9393 - loss: 0.1457
56/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9393 - loss: 0.1457
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9393 - loss: 0.1457
59/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9393 - loss: 0.1458
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9393 - loss: 0.1458
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9393 - loss: 0.1459
63/63 ━━━━━━━━━━━━━━━━━━━━ 4s 62ms/step - accuracy: 0.9393 - loss: 0.1459 - val_accuracy: 0.9851 - val_loss: 0.0497
Epoch 17/20
1/63 ━━━━━━━━━━━━━━━━━━━━ 4s 67ms/step - accuracy: 0.8750 - loss: 0.1786
2/63 ━━━━━━━━━━━━━━━━━━━━ 3s 51ms/step - accuracy: 0.8906 - loss: 0.1586
3/63 ━━━━━━━━━━━━━━━━━━━━ 3s 50ms/step - accuracy: 0.9028 - loss: 0.1517
4/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9095 - loss: 0.1488
6/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9159 - loss: 0.1470
8/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9202 - loss: 0.1443
9/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9222 - loss: 0.1436
10/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9234 - loss: 0.1432
11/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9247 - loss: 0.1426
12/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9257 - loss: 0.1418
14/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9276 - loss: 0.1412
16/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9296 - loss: 0.1404
17/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9307 - loss: 0.1397
18/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9318 - loss: 0.1391
19/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9330 - loss: 0.1384
20/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9341 - loss: 0.1377
22/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9359 - loss: 0.1364
23/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9366 - loss: 0.1360
24/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9373 - loss: 0.1357
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9379 - loss: 0.1353
26/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9385 - loss: 0.1350
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9391 - loss: 0.1346
28/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9396 - loss: 0.1342
30/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9406 - loss: 0.1337
31/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9410 - loss: 0.1336
32/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9413 - loss: 0.1335
34/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9420 - loss: 0.1333
35/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9423 - loss: 0.1332
36/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9426 - loss: 0.1330
37/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9429 - loss: 0.1329
38/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9431 - loss: 0.1327
39/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9434 - loss: 0.1325
40/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9436 - loss: 0.1324
42/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9440 - loss: 0.1323
43/63 ━━━━━━━━━━━━━━━━━━━━ 1s 50ms/step - accuracy: 0.9442 - loss: 0.1322
44/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9444 - loss: 0.1322
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9446 - loss: 0.1321
46/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9448 - loss: 0.1321
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9449 - loss: 0.1321
48/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9451 - loss: 0.1321
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9452 - loss: 0.1322
50/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9454 - loss: 0.1322
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9455 - loss: 0.1322
52/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9456 - loss: 0.1323
54/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9459 - loss: 0.1323
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9460 - loss: 0.1324
56/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9461 - loss: 0.1324
58/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9463 - loss: 0.1324
60/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9465 - loss: 0.1324
61/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9465 - loss: 0.1324
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9467 - loss: 0.1325
63/63 ━━━━━━━━━━━━━━━━━━━━ 4s 62ms/step - accuracy: 0.9468 - loss: 0.1325 - val_accuracy: 0.9814 - val_loss: 0.0517
Epoch 18/20
1/63 ━━━━━━━━━━━━━━━━━━━━ 4s 67ms/step - accuracy: 0.9688 - loss: 0.1182
2/63 ━━━━━━━━━━━━━━━━━━━━ 3s 52ms/step - accuracy: 0.9609 - loss: 0.1178
4/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9557 - loss: 0.1092
6/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9563 - loss: 0.1049
7/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9555 - loss: 0.1054
8/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9557 - loss: 0.1049
9/63 ━━━━━━━━━━━━━━━━━━━━ 2s 50ms/step - accuracy: 0.9552 - loss: 0.1050
10/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9553 - loss: 0.1050
11/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9555 - loss: 0.1047
12/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9557 - loss: 0.1043
13/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9558 - loss: 0.1044
14/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9559 - loss: 0.1047
15/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9561 - loss: 0.1048
16/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9563 - loss: 0.1048
17/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9564 - loss: 0.1049
18/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9563 - loss: 0.1056
19/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9562 - loss: 0.1066
20/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9562 - loss: 0.1073
21/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9562 - loss: 0.1078
22/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9562 - loss: 0.1084
23/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9561 - loss: 0.1090
24/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9561 - loss: 0.1095
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9561 - loss: 0.1099
26/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9560 - loss: 0.1105
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9558 - loss: 0.1110
28/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9557 - loss: 0.1114
30/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9556 - loss: 0.1119
31/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9555 - loss: 0.1122
32/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9554 - loss: 0.1124
33/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9554 - loss: 0.1126
34/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9553 - loss: 0.1127
35/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9553 - loss: 0.1129
36/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9553 - loss: 0.1131
38/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9552 - loss: 0.1133
39/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9552 - loss: 0.1133
40/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9551 - loss: 0.1135
42/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9551 - loss: 0.1136
43/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9550 - loss: 0.1137
44/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9549 - loss: 0.1138
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9549 - loss: 0.1140
46/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9548 - loss: 0.1141
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9547 - loss: 0.1142
48/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9547 - loss: 0.1143
50/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9547 - loss: 0.1145
52/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9547 - loss: 0.1146
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9548 - loss: 0.1147
54/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9548 - loss: 0.1147
56/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9549 - loss: 0.1148
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9549 - loss: 0.1149
58/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9549 - loss: 0.1150
60/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9549 - loss: 0.1151
62/63 ━━━━━━━━━━━━━━━━━━━━ 0s 50ms/step - accuracy: 0.9549 - loss: 0.1152
63/63 ━━━━━━━━━━━━━━━━━━━━ 4s 62ms/step - accuracy: 0.9549 - loss: 0.1154 - val_accuracy: 0.9889 - val_loss: 0.0417
Epoch 19/20
1/63 ━━━━━━━━━━━━━━━━━━━━ 4s 66ms/step - accuracy: 0.9062 - loss: 0.1450
2/63 ━━━━━━━━━━━━━━━━━━━━ 3s 51ms/step - accuracy: 0.9219 - loss: 0.1286
3/63 ━━━━━━━━━━━━━━━━━━━━ 3s 52ms/step - accuracy: 0.9340 - loss: 0.1224
4/63 ━━━━━━━━━━━━━━━━━━━━ 3s 51ms/step - accuracy: 0.9368 - loss: 0.1214
5/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9407 - loss: 0.1175
6/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9437 - loss: 0.1139
7/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9460 - loss: 0.1114
9/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9502 - loss: 0.1066
10/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9521 - loss: 0.1043
11/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9533 - loss: 0.1032
12/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9540 - loss: 0.1027
13/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9545 - loss: 0.1025
14/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9551 - loss: 0.1026
15/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9556 - loss: 0.1027
16/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9562 - loss: 0.1027
17/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9564 - loss: 0.1031
18/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9567 - loss: 0.1033
19/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9569 - loss: 0.1035
20/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9571 - loss: 0.1037
21/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9571 - loss: 0.1043
22/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9571 - loss: 0.1048
23/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9571 - loss: 0.1053
24/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9572 - loss: 0.1056
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9573 - loss: 0.1058
26/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9573 - loss: 0.1059
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9575 - loss: 0.1060
28/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9576 - loss: 0.1060
29/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9577 - loss: 0.1060
30/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9578 - loss: 0.1061
31/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9579 - loss: 0.1061
32/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9580 - loss: 0.1061
33/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9581 - loss: 0.1061
34/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9581 - loss: 0.1062
35/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9581 - loss: 0.1063
36/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9581 - loss: 0.1065
37/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9581 - loss: 0.1067
38/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9580 - loss: 0.1068
39/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9579 - loss: 0.1070
40/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9579 - loss: 0.1072
41/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9578 - loss: 0.1075
42/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9576 - loss: 0.1078
43/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9575 - loss: 0.1081
44/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9575 - loss: 0.1084
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9574 - loss: 0.1086
46/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9574 - loss: 0.1087
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9574 - loss: 0.1089
48/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9574 - loss: 0.1090
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9573 - loss: 0.1091
50/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9573 - loss: 0.1092
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9573 - loss: 0.1093
52/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9573 - loss: 0.1094
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9573 - loss: 0.1095
54/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9572 - loss: 0.1096
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9572 - loss: 0.1097
56/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9572 - loss: 0.1098
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9572 - loss: 0.1098
58/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9571 - loss: 0.1099
60/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9571 - loss: 0.1101
62/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9570 - loss: 0.1103
63/63 ━━━━━━━━━━━━━━━━━━━━ 4s 63ms/step - accuracy: 0.9569 - loss: 0.1105 - val_accuracy: 0.9851 - val_loss: 0.0428
Epoch 20/20
1/63 ━━━━━━━━━━━━━━━━━━━━ 4s 67ms/step - accuracy: 0.9375 - loss: 0.1219
2/63 ━━━━━━━━━━━━━━━━━━━━ 3s 51ms/step - accuracy: 0.9297 - loss: 0.1449
3/63 ━━━━━━━━━━━━━━━━━━━━ 3s 51ms/step - accuracy: 0.9323 - loss: 0.1463
4/63 ━━━━━━━━━━━━━━━━━━━━ 3s 51ms/step - accuracy: 0.9355 - loss: 0.1420
5/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9384 - loss: 0.1372
6/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9400 - loss: 0.1340
7/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9409 - loss: 0.1320
8/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9410 - loss: 0.1324
9/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9418 - loss: 0.1318
10/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9429 - loss: 0.1307
11/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9440 - loss: 0.1299
12/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9443 - loss: 0.1298
13/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9447 - loss: 0.1295
14/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9453 - loss: 0.1289
15/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9456 - loss: 0.1286
16/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9461 - loss: 0.1279
17/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9463 - loss: 0.1274
18/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9467 - loss: 0.1268
19/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9471 - loss: 0.1263
20/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9474 - loss: 0.1259
21/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9477 - loss: 0.1254
22/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9480 - loss: 0.1251
23/63 ━━━━━━━━━━━━━━━━━━━━ 2s 51ms/step - accuracy: 0.9483 - loss: 0.1249
24/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9485 - loss: 0.1247
25/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9487 - loss: 0.1244
26/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9489 - loss: 0.1242
27/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9491 - loss: 0.1241
28/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9492 - loss: 0.1239
29/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9494 - loss: 0.1237
30/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9495 - loss: 0.1235
31/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9497 - loss: 0.1233
32/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9499 - loss: 0.1231
33/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9501 - loss: 0.1229
34/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9502 - loss: 0.1226
35/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9504 - loss: 0.1224
36/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9506 - loss: 0.1222
37/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9507 - loss: 0.1220
38/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9508 - loss: 0.1219
39/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9509 - loss: 0.1218
40/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9509 - loss: 0.1218
41/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9509 - loss: 0.1219
42/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9509 - loss: 0.1220
43/63 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.9508 - loss: 0.1221
44/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9508 - loss: 0.1221
45/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9508 - loss: 0.1222
46/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9509 - loss: 0.1221
47/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9509 - loss: 0.1221
48/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9509 - loss: 0.1221
49/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9509 - loss: 0.1222
50/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9508 - loss: 0.1224
51/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9508 - loss: 0.1226
52/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9507 - loss: 0.1227
53/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9506 - loss: 0.1229
54/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9506 - loss: 0.1230
55/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9505 - loss: 0.1232
56/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9504 - loss: 0.1233
57/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9503 - loss: 0.1235
58/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9503 - loss: 0.1236
60/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9501 - loss: 0.1239
62/63 ━━━━━━━━━━━━━━━━━━━━ 0s 51ms/step - accuracy: 0.9500 - loss: 0.1241
63/63 ━━━━━━━━━━━━━━━━━━━━ 4s 63ms/step - accuracy: 0.9499 - loss: 0.1243 - val_accuracy: 0.9876 - val_loss: 0.0355
Let’s take a look at the learning curves of the training and validation accuracy/loss when fine-tuning the last few layers of the MobileNetV2 base model and training the classifier on top of it. The validation loss is much higher than the training loss, so you may get some overfitting.
You may also get some overfitting as the new training set is relatively small and similar to the original MobileNetV2 datasets.
After fine tuning the model nearly reaches 98% accuracy on the validation set.
acc += history_fine.history["accuracy"]
val_acc += history_fine.history["val_accuracy"]
loss += history_fine.history["loss"]
val_loss += history_fine.history["val_loss"]
plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label="Training Accuracy")
plt.plot(val_acc, label="Validation Accuracy")
plt.ylim([0.8, 1])
plt.plot(
[initial_epochs - 1, initial_epochs - 1], plt.ylim(), label="Start Fine Tuning"
)
plt.legend(loc="lower right")
plt.title("Training and Validation Accuracy")
plt.subplot(2, 1, 2)
plt.plot(loss, label="Training Loss")
plt.plot(val_loss, label="Validation Loss")
plt.ylim([0, 1.0])
plt.plot(
[initial_epochs - 1, initial_epochs - 1], plt.ylim(), label="Start Fine Tuning"
)
plt.legend(loc="upper right")
plt.title("Training and Validation Loss")
plt.xlabel("epoch")
plt.show()
Evaluation and prediction#
Finally you can verify the performance of the model on new data using test set.
loss, accuracy = model.evaluate(test_dataset)
print("Test accuracy :", accuracy)
Test accuracy : 0.96875
And now you are all set to use this model to predict if your pet is a cat or dog.
# Retrieve a batch of images from the test set
image_batch, label_batch = test_dataset.as_numpy_iterator().next()
predictions = model.predict_on_batch(image_batch).flatten()
predictions = tf.where(predictions < 0.5, 0, 1)
print("Predictions:\n", predictions.numpy())
print("Labels:\n", label_batch)
plt.figure(figsize=(10, 10))
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(image_batch[i].astype("uint8"))
plt.title(class_names[predictions[i]])
plt.axis("off")
Summary#
Using a pre-trained model for feature extraction: When working with a small dataset, it is a common practice to take advantage of features learned by a model trained on a larger dataset in the same domain. This is done by instantiating the pre-trained model and adding a fully-connected classifier on top. The pre-trained model is “frozen” and only the weights of the classifier get updated during training. In this case, the convolutional base extracted all the features associated with each image and you just trained a classifier that determines the image class given that set of extracted features.
Fine-tuning a pre-trained model: To further improve performance, one might want to repurpose the top-level layers of the pre-trained models to the new dataset via fine-tuning. In this case, you tuned your weights such that your model learned high-level features specific to the dataset. This technique is usually recommended when the training dataset is large and very similar to the original dataset that the pre-trained model was trained on.
To learn more, visit the Transfer learning guide.