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 npimport 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 = sess.run([W, b, loss], {x:x_train, y:y_train}) print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))
- tf.contrib.learn
reference link:
http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html
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