Create cifar10_cnn_keras_tensor.py

cifar10_cnn_keras_tensor.py

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+import tensorflow as tf
+from tensorflow.keras import datasets, layers, models
+import matplotlib.pyplot as plt
+
+# Load CIFAR-10 dataset
+(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
+
+# Normalize pixel values to be between 0 and 1
+train_images, test_images = train_images / 255.0, test_images / 255.0
+
+# Define the CNN model
+model = models.Sequential()
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(10))
+
+# Compile the model
+model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+
+# Define a callback to stop training when desired accuracy is achieved
+class AccuracyCallback(tf.keras.callbacks.Callback):
+    def on_epoch_end(self, epoch, logs={}):
+        if logs.get('val_accuracy') > 0.90:
+            print("\nReached 90% accuracy, stopping training...")
+            self.model.stop_training = True
+accuracy_callback = AccuracyCallback()
+
+# Train the model
+history = model.fit(train_images, train_labels, epochs=50, 
+                    validation_data=(test_images, test_labels),
+                    callbacks=[accuracy_callback])
+
+# Evaluate the model
+test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
+print('Test accuracy:', test_acc)
+
+# Plot accuracy and loss curves
+acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+loss = history.history['loss']
+val_loss = history.history['val_loss']
+epochs = range(len(acc))
+plt.figure(figsize=(10, 5))
+plt.subplot(1, 2, 1)
+plt.plot(epochs, acc, 'r', label='Training accuracy')
+plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
+plt.title('Training and validation accuracy')
+plt.legend()
+plt.subplot(1, 2, 2)
+plt.plot(epochs, loss, 'r', label='Training loss')
+plt.plot(epochs, val_loss, 'b', label='Validation loss')
+plt.title('Training and validation loss')
+plt.legend()
+plt.show()