Building Neural Networks with TensorFlow 2.x

TensorFlow 2.x has revolutionized deep learning development with its intuitive Keras API, eager execution by default, and seamless deployment capabilities.

Building Your First Neural Network

import tensorflow as tf
from tensorflow import keras

model = keras.Sequential([
    keras.layers.Dense(128, activation='relu', input_shape=(784,)),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(10, activation='softmax')
])

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

The Functional API

inputs = keras.Input(shape=(784,))
x = keras.layers.Dense(128, activation='relu')(inputs)
x = keras.layers.Dropout(0.2)(x)
x = keras.layers.Dense(64, activation='relu')(x)
outputs = keras.layers.Dense(10, activation='softmax')(x)

model = keras.Model(inputs=inputs, outputs=outputs)

Training Best Practices

callbacks = [
    keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True),
    keras.callbacks.ModelCheckpoint('best_model.h5', save_best_only=True),
    keras.callbacks.ReduceLROnPlateau(factor=0.5, patience=5)
]

history = model.fit(
    X_train, y_train,
    validation_split=0.2,
    epochs=100,
    batch_size=32,
    callbacks=callbacks
)

CNN Architecture

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

Transfer Learning

base_model = keras.applications.MobileNetV2(
    input_shape=(224, 224, 3),
    include_top=False,
    weights='imagenet'
)
base_model.trainable = False

model = keras.Sequential([
    base_model,
    keras.layers.GlobalAveragePooling2D(),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(num_classes, activation='softmax')
])

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