Keras 多层感知机多类别的 softmax 分类模型代码

Multilayer Perceptron (MLP) for multi-class softmax classification:

from keras.models import Sequential

from keras.layers import Dense, Dropout, Activation

from keras.optimizers import SGD

# 生成随机数据

import numpy as np

x_train = np.random.random((1000, 20))

y_train = keras.utils.to_categorical(np.random.randint(10, size=(1000, 1)), num_classes=10)

x_test = np.random.random((100, 20))

y_test = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)

model = Sequential()

# Dense(64) is a fully-connected layer with 64 hidden units.

# in the first layer, you must specify the expected input data shape:

# here, 20-dimensional vectors.

model.add(Dense(64, activation='relu', input_dim=20))

model.add(Dropout(0.5))

model.add(Dense(64, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(10, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(loss='categorical_crossentropy',

              optimizer=sgd,

              metrics=['accuracy'])

model.fit(x_train, y_train,

          epochs=20,

          batch_size=128)

score = model.evaluate(x_test, y_test, batch_size=128)

https://keras.io/getting-started/sequential-model-guide/

更多教程：http://www.tensorflownews.com/

巴特西

Keras 多层感知机多类别的 softmax 分类模型代码

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巴特西

Keras 多层感知机 多类别的 softmax 分类模型代码

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Keras 多层感知机多类别的 softmax 分类模型代码