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Training Keras model with tf.data
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| """An example of how to use tf.Dataset in Keras Model""" | |
| import tensorflow as tf # only work from tensorflow==1.9.0-rc1 and after | |
| _EPOCHS = 5 | |
| _NUM_CLASSES = 10 | |
| _BATCH_SIZE = 128 | |
| def training_pipeline(): | |
| # ############# | |
| # Load Dataset | |
| # ############# | |
| (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() | |
| training_set = tfdata_generator(x_train, y_train, is_training=True, batch_size=_BATCH_SIZE) | |
| testing_set = tfdata_generator(x_test, y_test, is_training=False, batch_size=_BATCH_SIZE) | |
| # ############# | |
| # Train Model | |
| # ############# | |
| model = keras_model() # your keras model here | |
| model.compile('adam', 'categorical_crossentropy', metrics=['acc']) | |
| model.fit( | |
| training_set.make_one_shot_iterator(), | |
| steps_per_epoch=len(x_train) // _BATCH_SIZE, | |
| epochs=_EPOCHS, | |
| validation_data=testing_set.make_one_shot_iterator(), | |
| validation_steps=len(x_test) // _BATCH_SIZE, | |
| verbose = 1) | |
| def tfdata_generator(images, labels, is_training, batch_size=128): | |
| '''Construct a data generator using tf.Dataset''' | |
| def preprocess_fn(image, label): | |
| '''A transformation function to preprocess raw data | |
| into trainable input. ''' | |
| x = tf.reshape(tf.cast(image, tf.float32), (28, 28, 1)) | |
| y = tf.one_hot(tf.cast(label, tf.uint8), _NUM_CLASSES) | |
| return x, y | |
| dataset = tf.data.Dataset.from_tensor_slices((images, labels)) | |
| if is_training: | |
| dataset = dataset.shuffle(1000) # depends on sample size | |
| # Transform and batch data at the same time | |
| dataset = dataset.apply(tf.contrib.data.map_and_batch( | |
| preprocess_fn, batch_size, | |
| num_parallel_batches=4, # cpu cores | |
| drop_remainder=True if is_training else False)) | |
| dataset = dataset.repeat() | |
| dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE) | |
| return dataset | |
| def keras_model(): | |
| from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, Input | |
| inputs = Input(shape=(28, 28, 1)) | |
| x = Conv2D(32, (3, 3),activation='relu', padding='valid')(inputs) | |
| x = MaxPool2D(pool_size=(2, 2))(x) | |
| x = Conv2D(64, (3, 3), activation='relu')(x) | |
| x = MaxPool2D(pool_size=(2, 2))(x) | |
| x = Flatten()(x) | |
| x = Dense(512, activation='relu')(x) | |
| x = Dropout(0.5)(x) | |
| outputs = Dense(_NUM_CLASSES, activation='softmax')(x) | |
| return tf.keras.Model(inputs, outputs) | |
| if __name__ == '__main__': | |
| training_pipeline() |
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