1a

import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import numpy as np import matplotlib.pyplot as plt (x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()

1b

x_train = x_train / 255.0 x_test = x_test / 255.0

1c

print("x_train shape:", x_train.shape) print("y_train shape:", y_train.shape) print("x_test shape:", x_test.shape) print("y_test shape:", y_test.shape) plt.figure(figsize=(10, 4)) for i in range(10): plt.subplot(2, 5, i+1) plt.imshow(x_train[i]) plt.title(f"Label: {y_train[i]}") plt.tight_layout() plt.show()

2a

model = keras.Sequential([ layers.Conv2D(64, (3, 3), activation='relu', input_shape=(32, 32, 3)), layers.MaxPooling2D((2, 2)), layers.Conv2D(128, (3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Flatten(), layers.Dense(256, activation='relu'), layers.Dense(100) ])

2b

model.compile(optimizer='adagrad', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) history = model.fit(x_train, y_train, epochs=12, batch_size=128, validation_split=0.2)

3a

plt.plot(history.history['accuracy'], label='training accuracy') plt.plot(history.history['val_accuracy'], label='validation accuracy') plt.title('Accuracy') plt.xlabel('Epochs') plt.ylabel('Accuracy') plt.legend() plt.show() plt.plot(history.history['loss'], label='training loss') plt.plot(history.history['val_loss'], label='validation loss') plt.title('Loss') plt.xlabel('Epochs') plt.ylabel('Loss') plt.legend() plt.show()

test_loss_2, test_acc_2 = model.evaluate(x_test, y_test, verbose=2)

import numpy as np from tensorflow.keras.datasets import imdb from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Embedding, LSTM, Dense from tensorflow.keras.preprocessing import sequence

Set random seed for reproducibility

np.random.seed(42)

Set the maximum number of words to be used (vocabulary size)

max_words = 5000

Load the IMDB dataset

(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_words)

Pad sequences to a fixed length

max_length = 100 x_train = sequence.pad_sequences(x_train, maxlen=max_length) x_test = sequence.pad_sequences(x_test, maxlen=max_length)

Create the RNN model

model = Sequential() model.add(Embedding(max_words, 32, input_length=max_length)) model.add(LSTM(100)) model.add(Dense(1, activation='sigmoid'))

Compile the model

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

Print the model summary

print(model.summary())

Train the model

batch_size = 64 epochs = 5 model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test))

Evaluate the model

scores = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', scores[0])

print('Test accuracy:', scores[1])

import numpy as np from tensorflow import keras from tensorflow.keras import layers

Load the MNIST dataset

(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()

Normalize and reshape the input data

x_train = x_train.astype('float32') / 255.0 x_test = x_test.astype('float32') / 255.0 x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))

Define the autoencoder model

input_img = keras.Input(shape=(28, 28, 1)) encoded = layers.Flatten()(input_img) encoded = layers.Dense(64, activation='relu')(encoded) decoded = layers.Dense(784, activation='sigmoid')(encoded) decoded = layers.Reshape((28, 28, 1))(decoded) autoencoder = keras.Model(input_img, decoded) autoencoder.compile(optimizer='adam', loss='binary_crossentropy') autoencoder.summary() from tensorflow.keras.utils import plot_model plot_model(autoencoder, to_file='model_visualization.png', show_shapes=True)

Train the autoencoder

autoencoder.fit(x_train, x_train, epochs=10, batch_size=128, shuffle=True, validation_data=(x_test, x_test))

Test the autoencoder

decoded_imgs = autoencoder.predict(x_test)

Display some results

import matplotlib.pyplot as plt n = 10 # Number of digits to display plt.figure(figsize=(20, 4)) for i in range(n):

Original images

ax = plt.subplot(2, n, i + 1) plt.imshow(x_test[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False)

Decoded images

ax = plt.subplot(2, n, i + 1 + n) plt.imshow(decoded_imgs[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plt.show()