import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' import tensorflow as tf def linearregression(): X = tf.random_normal([100,1],mean=0.0,stddev=1.0) y_true = tf.matmul(X,[[0.8]]) + [[0.7]] weights = tf.Variable(initial_value=tf.random_normal([1,1])) bias = tf.Variable(initial_value=tf.random_normal([1,1])) y_predict = tf.matmul(X,weights)+bias loss = tf.reduce_mean(tf.square(y_predict-y_true)) optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for i in range(1000): sess.run(optimizer) print("loss:", sess.run(loss)) print("weight:", sess.run(weights)) print("bias:", sess.run(bias)) if __name__ == '__main__': linearregression()
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