import tensorflow as tf | |
model = tf.keras.models.Sequential([ | |
tf.keras.layers.Flatten(input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)), | |
tf.keras.layers.Dense(300, activation="relu"), | |
tf.keras.layers.Dense(100, activation="relu"), | |
tf.keras.layers.Dense(len(CLASS_NAMES), activation="softmax") | |
]) | |
model.compile(optimizer='adam', | |
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), | |
metrics=['accuracy']) | |
log_dir = "logs\\fit\\" + 'ageron_' + 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] | |
) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment