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
from keras.optimizers import SGD

(x_train, y_train), (x_test, y_test) = mnist.load_data()
# (60000,28,28)
print('x_shape:', x_train.shape)
# (60000)
print('y_shape', y_train.shape)
# (60000,,28,28)->(60000,784)
x_train = x_train.reshape(x_train.shape[0], -1)/255.0
x_test = x_test.reshape(x_test.shape[0], -1)/255.0

# 换one hot形式
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

model = Sequential([
    Dense(units=10,input_dim=784,bias_initializer='one',activation='softmax')
])

# 定义优化器
sgd = SGD(lr=0.2)

# 定义优化器，loss function, 训练过程中计算准确率
model.compile(
    optimizer=sgd,
    loss='mse',
    metrics=['accuracy']
)

# 训练模型
model.fit(x_train, y_train, batch_size=64, epochs=5)

# 评估模型
loss, accuracy = model.evaluate(x_test, y_test)

print('\ntest loss', loss)
print('accuracy', accuracy)

# 保存模型
model.save('model.h5')

