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50 lines
1.2 KiB
Python
50 lines
1.2 KiB
Python
# https://keras.io/getting-started/sequential-model-guide/
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# Multilayer Perceptron (MLP) for multi-class softmax classification
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# 用于多层softmax分类的多层感知器（MLP）
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import numpy as np
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from tensorflow import keras
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense, Dropout, Activation
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from tensorflow.keras.optimizers import SGD
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x_train = np.random.random((1000, 20))
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y_train = keras.utils.to_categorical(
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np.random.randint(10, size=(1000, 1)),
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num_classes=10
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)
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x_test = np.random.random((100, 20))
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y_test = keras.utils.to_categorical(
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np.random.randint(10, size=(100, 1)),
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num_classes=10
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)
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model = Sequential()
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model.add(Dense(64, activation='relu', input_dim=20))
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model.add(Dropout(0.5))
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model.add(Dense(64, activation='relu'))
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model.add(Dropout(0.5))
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model.add(Dense(10, activation='softmax'))
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sgd = SGD(
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lr=0.01,
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decay=1e-6,
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momentum=0.9,
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nesterov=True
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)
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model.compile(
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loss='categorical_crossentropy',
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optimizer=sgd,
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metrics=['accuracy']
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)
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model.fit(
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x_train,
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y_train,
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epochs=20,
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batch_size=128
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)
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score = model.evaluate(x_test, y_test, batch_size=128)
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