tensorflow_model_design_cnn_tutorial.py

import tensorflow as tf

model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(len(CLASS_NAMES))
])

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

# Setup the tensorboard connect
log_dir = "logs\\fit\\" + 'tensorflowtuto_' + 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]
)