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main.py

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+import tensorflow as tf
+import tensorflow_datasets as tfds
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.keras import regularizers
+
+assert 'COLAB_TPU_ADDR' in os.environ, 'Missin TPU?'
+if('COLAB_TPU_ADDR') in os.environ:
+  TF_MASTER = 'grpc://{}'.format(os.environ['COLAB_TPU_ADDR'])
+else:
+  TF_MASTER = ''
+tpu_address = TF_MASTER
+
+resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu_address)
+tf.config.experimental_connect_to_cluster(resolver)
+tf.tpu.experimental.initialize_tpu_system(resolver)
+
+
+strategy = tf.distribute.TPUStrategy(resolver)
+
+
+def create_model():
+  return tf.keras.Sequential([
+        tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
+        tf.keras.layers.BatchNormalization(),
+        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
+        tf.keras.layers.BatchNormalization(),
+        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
+        tf.keras.layers.Dropout(0.25),
+
+        tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
+        tf.keras.layers.BatchNormalization(),
+        tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_regularizer=regularizers.l2(0.001)),
+        tf.keras.layers.BatchNormalization(),
+        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
+        tf.keras.layers.Dropout(0.25),
+
+        tf.keras.layers.Flatten(),
+        tf.keras.layers.Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.001)),
+        tf.keras.layers.BatchNormalization(),
+        tf.keras.layers.Dropout(0.5),
+        tf.keras.layers.Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.001)),
+        tf.keras.layers.BatchNormalization(),
+        tf.keras.layers.Dropout(0.5),
+        tf.keras.layers.Dense(10, activation='softmax')
+  ])
+
+
+def get_dataset(batch_size, is_training=True):
+    split = 'train' if is_training else 'test'
+    dataset, info = tfds.load(name='mnist', split=split, with_info= True, as_supervised=True, try_gcs=True)
+    def scale(image, label):
+        image = tf.cast(image, tf.float32)
+        image /= 255.0
+        return image, label
+        dataset = dataset.map(scale)
+    if is_training:
+        dataset = dataset.shuffle(10000)
+        dataset = dataset.repeat()
+    dataset = dataset.batch(batch_size)
+    return dataset
+
+
+
+with strategy.scope():
+    model = create_model()
+    model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['sparse_categorical_accuracy'])
+    model.summary()
+
+  
+
+
+batch_size = 512
+train_dataset = get_dataset(batch_size, True)
+validation_dataset = get_dataset(batch_size, False)
+with strategy.scope():
+    model = create_model()
+    model.compile(optimizer='adam', steps_per_execution=50, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['sparse_categorical_accuracy'])
+    epochs = 80
+    steps_per_epoch = 60000 // batch_size
+    validation_steps = 10000 // batch_size
+    history = model.fit(train_dataset, epochs=epochs, steps_per_epoch=steps_per_epoch, validation_data=validation_dataset, validation_steps=validation_steps)
+  
+
+acc = history.history['sparse_categorical_accuracy']
+val_acc = history.history['val_sparse_categorical_accuracy']
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+epochs_range = range(epochs)
+
+
+plt.figure(figsize=(15, 15))
+plt.subplot(2, 2, 1)
+plt.plot(epochs_range, acc, label='Training Accuracy')
+plt.plot(epochs_range, val_acc, label='Validation Accuracy')
+plt.legend(loc='lower right')
+plt.title('Training and Validation Accuracy')
+
+plt.subplot(2, 2, 2)
+plt.plot(epochs_range, loss, label='Training Loss')
+plt.plot(epochs_range, val_loss, label='Validation Loss')
+plt.legend(loc='upper right')
+plt.title('Training and Validation Loss')
+plt.show()
+
+
+final_daset = validation_dataset.take(10)
+test_images, test_labels = next(iter(final_daset.take(10)))
+class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
+
+# Получение предсказаний нейросети для 10 изображений
+predictions = model.predict(test_images)
+
+fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(15, 6),
+                         subplot_kw={'xticks': [], 'yticks': []})
+for i, ax in enumerate(axes.flat):
+    # Отображение изображения
+    ax.imshow(test_images[i])
+    # Отображение меток и предсказаний
+    true_label = class_names[test_labels[i]]
+    pred_label = class_names[np.argmax(predictions[i])]
+    if true_label == pred_label:
+        ax.set_title("Это: {}, ИИ: {}".format(true_label, pred_label), color='green')
+    else:
+        ax.set_title("Это: {}, ИИ: {}".format(true_label, pred_label), color='red')
+
+plt.tight_layout()
+plt.show()