import numpy as np from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense, Activation from keras.optimizers import SGD, RMSprop from keras.models import load_model (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.reshape(x_train.shape[0], -1) / 255 x_test = x_test.reshape(x_test.shape[0], -1) / 255 y_train = np_utils.to_categorical(y_train, 10) y_test = np_utils.to_categorical(y_test, 10) model = Sequential([ Dense(32, input_dim=784), Activation('relu'), Dense(10), Activation('softmax') ]) # 定义优化器 rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0) model.compile( optimizer=rmsprop, loss='categorical_crossentropy', # 得到损失率 metrics=['accuracy'] ) # (60000/32)=训练次数 然后再来100次循环 model.fit(x_train, y_train, batch_size=32, epochs=1) loss, accuracy = model.evaluate(x_test, y_test) print('loss:', loss, 'accuracy:', accuracy) print(y_test[0]) x_test_element = x_test[0].reshape(-1, 784) Y_pred = model.predict(x_test_element, batch_size=1) print(Y_pred)
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