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tensorflow 學習之(2＿
tf.train API 解 equation, y = W*x +b , The completed trainable linear regression model is shown here: import numpy as np import tensorflow as tf # Model parameters W = tf . Variable ([. 3 ], dtype = tf . float32 ) b = tf . Variable ([-. 3 ], dtype = tf . float32 ) # Model input and output x = tf . placeholder ( tf . float32 ) linear_model = W * x + b y = tf . placeholder ( tf . float32 ) # loss loss = tf . reduce_sum ( tf . square ( linear_model - y )) # sum of the squares # optimizer optimizer = tf . train . GradientDescentOptimizer ( 0.01 ) train = optimizer . minimize ( loss ) # training data x_train = [ 1 , 2 , 3 , 4 ] y_train = [ 0 ,- 1 ,- 2 ,- 3 ] # training loop init = tf . global_variables_initializer () sess = tf . Session () sess . run ( init ) # reset values to wrong for i in range ( 1000 ): sess . run ( train , { x : x_train , y : y_train }) # evaluate training accuracy curr_W , curr_b , curr_loss...
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