华为云使用手写的keras训练代码

构建环境

!pip install --upgrade keras_applications==1.0.6 keras==2.2.4
import numpy as np
import matplotlib.pyplot as plt
import keras

构建模拟数据

# create some data
X = np.linspace(-1, 1, 200)
np.random.shuffle(X)    # randomize the data
Y = 0.5 * X + 2 + np.random.normal(0, 0.05, (200, ))
# plot data
plt.scatter(X, Y)
plt.show()
X_train, Y_train = X[:160], Y[:160]     # train 前 160 data points
X_test, Y_test = X[160:], Y[160:]       # test 后 40 data points

构建模型

from keras.models import Sequential

# 可以简单地使用 .add() 来堆叠模型：
from keras.layers import Dense

model = Sequential()
model.add(Dense(output_dim=1, input_dim=1))

配置学习过程

model.compile(loss='mse', optimizer='sgd')

训练

print('Training -----------')
for step in range(301):
    cost = model.train_on_batch(X_train, Y_train)
    if step % 100 == 0:
        print('train cost: ', cost)

评估

loss_and_metrics = model.evaluate(X_test, Y_test, batch_size=128)
print(loss_and_metrics)

可视化结果

# plotting the prediction
Y_pred = model.predict(X_test)
plt.scatter(X_test, Y_test, s=5)
plt.scatter(X_test, Y_pred)
plt.show()