Multilayer Perceptron (MLP) for multi-class softmax classification:
from keras.models import Sequentialfrom keras.layers import Dense, Dropout, Activationfrom keras.optimizers import SGD# 生成随机数据import numpy as npx_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)pile(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/
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