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https://github.com/aladdinpersson/Machine-Learning-Collection.git
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62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
import os
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras.datasets import mnist
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physical_devices = tf.config.list_physical_devices("GPU")
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tf.config.experimental.set_memory_growth(physical_devices[0], True)
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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# x_train = x_train.reshape(-1, 784).astype("float32") / 255.0
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# x_test = x_test.reshape(-1, 784).astype("float32") / 255.0
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x_train = x_train.reshape([-1, 28, 28]).astype("float32") / 255.0
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x_test = x_test.reshape([-1, 28, 28]).astype("float32") / 255.0
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model = keras.Sequential()
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model.add(keras.Input(shape=(None, 28)))
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model.add(layers.SimpleRNN(512, return_sequences=True, activation="relu"))
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model.add(layers.SimpleRNN(512, activation="relu"))
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model.add(layers.Dense(10))
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model = keras.Sequential()
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model.add(keras.Input(shape=(None, 28)))
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model.add(layers.SimpleRNN(256, return_sequences=True, activation="tanh"))
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model.add(layers.SimpleRNN(256))
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model.add(layers.Dense(10))
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model = keras.Sequential()
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model.add(keras.Input(shape=(None, 28)))
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model.add(layers.GRU(256, return_sequences=True, activation="relu"))
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model.add(layers.GRU(256))
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model.add(layers.Dense(10))
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model = keras.Sequential()
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model.add(keras.Input(shape=(None, 28)))
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model.add(
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layers.Bidirectional(layers.LSTM(256, return_sequences=True, activation="relu"))
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)
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model.add(layers.LSTM(256, name="lstm_layer2"))
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model.add(layers.Dense(10))
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model = keras.Sequential()
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model.add(keras.Input(shape=(None, 28)))
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model.add(
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layers.Bidirectional(layers.LSTM(256, return_sequences=True, activation="relu"))
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)
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model.add(layers.Bidirectional(layers.LSTM(256, name="lstm_layer2")))
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model.add(layers.Dense(10))
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print(model.summary())
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model.compile(
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loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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optimizer=keras.optimizers.Adam(lr=0.001),
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metrics=["accuracy"],
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)
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model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=2)
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model.evaluate(x_test, y_test, batch_size=64, verbose=2)
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