Category: Examples

• Fine-tuning a Pretrained Model: Use models like VGG16 or ResNet50 pre-trained on ImageNet and fine-tune for a specific task, such as custom image classification.

pythonCopy codefrom tensorflow.keras.applications import VGG16
from tensorflow.keras import layers, models

# Load VGG16 without the top layer
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))

# Freeze the base model
base_model.trainable = False

# Add custom layers
model = models.Sequential([
base_model,
layers.Flatten(),
layers.Dense(256, activation='relu'),
layers.Dense(1, activation='sigmoid')
])

# Compile and train the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

• Temperature Forecasting: Predicting future temperatures based on past values using LSTMs or GRUs.
• Stock Price Prediction: Using RNNs or LSTMs to predict stock prices based on historical data.

• Variational Autoencoders (VAEs): Keras has examples for creating a variational autoencoder that generates new images by learning the latent space of the input data.
• Generative Adversarial Networks (GANs): A more advanced model that pits two networks against each other: a generator and a discriminator.

pythonCopy codefrom tensorflow.keras import layers, models, backend as K
from tensorflow.keras.losses import binary_crossentropy

# Sampling function for VAE
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], K.int_shape(z_mean)[1]))
return z_mean + K.exp(0.5 * z_log_var) * epsilon
# Build encoder
inputs = layers.Input(shape=(28, 28, 1))
x = layers.Conv2D(32, 3, activation="relu", padding="same")(inputs)
z_mean = layers.Dense(2, name="z_mean")(x)
z_log_var = layers.Dense(2, name="z_log_var")(x)
z = layers.Lambda(sampling, output_shape=(2,), name="z")([z_mean, z_log_var])

# Build decoder
decoder = models.Sequential([
layers.InputLayer(input_shape=(2,)),
layers.Dense(128, activation='relu'),
layers.Reshape((4, 4, 8)),
layers.Conv2DTranspose(32, 3, activation='relu'),
layers.Conv2D(1, 3, activation='sigmoid')
])

# Define the VAE
vae = models.Model(inputs, decoder(z))
vae.compile(optimizer='adam', loss='mse')

• IMDB Sentiment Analysis: Using Keras for binary sentiment classification (positive/negative) on the IMDB movie review dataset with recurrent neural networks (RNNs) or LSTMs.

pythonCopy codefrom tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras import layers, models

# Load IMDB dataset
max_features = 10000
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)

# Pad sequences to ensure uniform input size
maxlen = 500
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)

# Build LSTM model
model = models.Sequential([
layers.Embedding(max_features, 128, input_length=maxlen),
layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2),
layers.Dense(1, activation='sigmoid')
])

# Compile and train the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=3, batch_size=64, validation_data=(x_test, y_test))

• MNIST Handwritten Digits: Classifying the famous MNIST dataset, which contains images of handwritten digits (0–9). This is a beginner-friendly example that introduces how to use convolutional neural networks (CNNs) for image classification.
• CIFAR-10 Image Classification: Building a CNN to classify the CIFAR-10 dataset, a set of 60,000 32×32 color images in 10 classes.

pythonCopy codefrom tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist

# Load MNIST dataset
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Normalize pixel values
train_images, test_images = train_images / 255.0, test_images / 255.0

# Build CNN model
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax')
])

# Compile and train the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, validation_data=(test_images, test_labels))