def train(self, train_X, train_Y, learning_rate, training_epochs, model_output_dir=None):
n_samples = train_X.shape[0]
# Mean squared error
cost = tf.reduce_sum(tf.pow(self.model - self.vars['Y'], 2)) / (2 * n_samples)
# Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Launch the graph
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
# Fit all training data
for epoch in range(training_epochs):
for x, y in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={self.vars['X']: x, self.vars['Y']: y})
# Save model locally
saver.save(sess, model_output_dir + 'model.ckpt')
return
tf_regression.py 文件源码
python
阅读 25
收藏 0
点赞 0
评论 0
评论列表
文章目录