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Aladdin Persson
2021-01-30 21:49:15 +01:00
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ML/TensorFlow/Basics/tutorial6-rnn-gru-lstm.py Normal file
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import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist
physical_devices = tf.config.list_physical_devices("GPU")
tf.config.experimental.set_memory_growth(physical_devices[0], True)
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# x_train = x_train.reshape(-1, 784).astype("float32") / 255.0
# x_test = x_test.reshape(-1, 784).astype("float32") / 255.0
x_train = x_train.reshape([-1, 28, 28]).astype("float32") / 255.0
x_test = x_test.reshape([-1, 28, 28]).astype("float32") / 255.0
model = keras.Sequential()
model.add(keras.Input(shape=(None, 28)))
model.add(layers.SimpleRNN(512, return_sequences=True, activation="relu"))
model.add(layers.SimpleRNN(512, activation="relu"))
model.add(layers.Dense(10))
model = keras.Sequential()
model.add(keras.Input(shape=(None, 28)))
model.add(layers.SimpleRNN(256, return_sequences=True, activation="tanh"))
model.add(layers.SimpleRNN(256))
model.add(layers.Dense(10))
model = keras.Sequential()
model.add(keras.Input(shape=(None, 28)))
model.add(layers.GRU(256, return_sequences=True, activation="relu"))
model.add(layers.GRU(256))
model.add(layers.Dense(10))
model = keras.Sequential()
model.add(keras.Input(shape=(None, 28)))
model.add(
layers.Bidirectional(layers.LSTM(256, return_sequences=True, activation="relu"))
)
model.add(layers.LSTM(256, name="lstm_layer2"))
model.add(layers.Dense(10))
model = keras.Sequential()
model.add(keras.Input(shape=(None, 28)))
model.add(
layers.Bidirectional(layers.LSTM(256, return_sequences=True, activation="relu"))
)
model.add(layers.Bidirectional(layers.LSTM(256, name="lstm_layer2")))
model.add(layers.Dense(10))
print(model.summary())
model.compile(
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer=keras.optimizers.Adam(lr=0.001),
metrics=["accuracy"],
)
model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=2)
model.evaluate(x_test, y_test, batch_size=64, verbose=2)