Introduction to Convolutional Neural Networks with Weights & Biases

# Define model
model = tf.keras.models.Sequential()

# Conv2D adds a convolution layer with 32 filters that generates 2 dimensional 
feature maps to learn different aspects of our image
model.add(tf.keras.layers.Conv2D(32, (3, 3), padding='same',
                                input_shape=X_train.shape[1:], activation='relu'))

# MaxPooling2D layer reduces the size of the image representation our 
convolutional layers learnt, and in doing so it reduces the number of parameters 
and computations the network needs to perform.
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))

# Dropout layer turns off a percentage of neurons at every step
model.add(tf.keras.layers.Dropout(config.dropout))

# Flattens our array so we can feed the convolution layer outputs (a matrix) 
into our fully connected layer (an array)
model.add(tf.keras.layers.Flatten())

# Dense layer creates dense, fully connected layers with x inputs and y outputs 
- it simply outputs the dot product of our inputs and weights
model.add(tf.keras.layers.Dense(config.dense_layer_nodes, activation='relu'))
model.add(tf.keras.layers.Dropout(config.dropout))
model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))

# Compile the model and specify the optimizer and loss function
model.compile(loss='categorical_crossentropy',
             optimizer=tf.keras.optimizers.Adam(config.learn_rate),
             metrics=['accuracy'])

# Fit the model to the training data, specify the batch size and the 
WandbCallback() to track model
model.fit(X_train, y_train, epochs=10, batch_size=128, validation_data=(X_test, 
y_test),
         callbacks=[wandb.keras.WandbCallback(data_type="image", 
labels=class_names, save_model=False)])

# Define the model (same as above)
...

# Compile the model
model.compile(loss='categorical_crossentropy',
             optimizer="adam",
             metrics=['accuracy'])


# Add data augmentation
datagen = ImageDataGenerator(
   featurewise_center=False,  # set input mean to 0 over the dataset
   samplewise_center=False,  # set each sample mean to 0
   featurewise_std_normalization=False,  # divide inputs by std of the dataset
   samplewise_std_normalization=False,  # divide each input by its std
   zca_whitening=False,  # apply ZCA whitening
   rotation_range=15,  # randomly rotate images in the range (degrees, 0 to 180)
   width_shift_range=0.1,  # randomly shift images horizontally (fraction of 
total width)
   height_shift_range=0.1,  # randomly shift images vertically (fraction of 
total height)
   horizontal_flip=True,  # randomly flip images
   vertical_flip=False)  # randomly flip images
datagen.fit(X_train)

# Fit the model on the batches generated by datagen.flow()
model.fit_generator(datagen.flow(X_train, y_train,
                                batch_size=config.batch_size),
                   steps_per_epoch=X_train.shape[0] // config.batch_size,
                   epochs=config.epochs,
                   validation_data=(X_test, y_test),
                   callbacks=[WandbCallback(data_type="image", 
labels=class_names)])