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] | |
) |
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