

# gooyoo




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 = 1);

print(model.evaluate(x_test, y_test, batch_size = 64, verbose = 2));



