tensorflow_model_design_cnn_pml.py

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(
        32, (3, 3), padding='same', activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Dropout(rate=0.5),
    
    tf.keras.layers.Conv2D(
        64, (3, 3), padding='same', activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Dropout(rate=0.5),
    
    tf.keras.layers.Conv2D(
        128, (3, 3), padding='same', activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    
    tf.keras.layers.Conv2D(
        256, (3, 3), padding='same', activation='relu'),
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(len(CLASS_NAMES), activation=None)
])

model.build(input_shape=(None, IMG_HEIGHT, IMG_WIDTH, 3))

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

log_dir = "logs\\fit\\" + 'pml_' + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

history = model.fit(
train_data_gen,
steps_per_epoch=image_count_train // BATCH_SIZE,
epochs=EPOCHS,
validation_data=val_data_gen,
validation_steps=image_count_validation // BATCH_SIZE,
callbacks=[tensorboard_callback]
)