tf11_build_neural_network.py

tf11_build_neural_network.py

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+##tensorflow
+#tf11_build_neural_network.py
+
+import tensorflow as tf
+import numpy as np
+
+def add_layer(inputs, in_size, out_size, activation_function=None):
+    # add one more layer and return the output of this layer
+    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
+    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
+    Wx_plus_b = tf.matmul(inputs, Weights) + biases
+    if activation_function is None:
+        outputs = Wx_plus_b
+    else:
+        outputs = activation_function(Wx_plus_b)
+    return outputs
+
+x_data=np.linspace(-1,1,300)[:,np.newaxis]
+noise=np.random.normal(0,0.05,x_data.shape)
+y_data=np.square(x_data)-0.5+noise
+
+xs=tf.placeholder(tf.float32,[None,1])
+ys=tf.placeholder(tf.float32,[None,1])
+
+l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
+predition=add_layer(l1,10,1,activation_function=None)
+
+loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-predition),
+                    #reducition_indices=[1]))
+                    reduction_indices=[1]))
+train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)
+
+init=tf.initialize_all_variables()
+sess=tf.Session()
+sess.run(init)
+
+for i in range(1000):
+    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
+    if i%50 ==0:
+        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))