Beginner TensorFlow

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import tensorflow as tf import numpy as np import matplotlib.pyplot as plt def normalize(X): """"Normalize the array""" mean = np.mean(X) std = np.std(X); X = (X-mean)/std return X boston = tf.contrib.learn.datasets.load_dataset("boston") # http://c.biancheng.net/view/1906.html boston = tf.contrib.learn.datasets.load_dataset('boston'Import data#) X_train, Y_train boston.data = [:,. 5], boston.target # data samples assignment N_SAMPLES = len (X_train) # reads the number of the training set X-tf.placeholder = (tf.float32, name = ' X- ' ) # variable Definition model =====> model W * = X + Y B the Y = tf.placeholder (tf.float32, name = ' the Y ' ) B = tf.Variable (0.0 ) W = tf.Variable ( 0.0 ) # define end Y_hat = * W + X- B loss = tf.square (the Y - Y_hat, name = ' loss ' ) # define loss function = tf.train.GradientDescentOptimizer Optimizer (learning_rate = 0.01) .minimize (Loss) # optimal solution for the loss function, the present process is the key to solving the optimal operational function init_op tf.global_variables_initializer = () # initialize Total = [] tf.Session with () AS Sess: # use with the method calculates the tf.Session () returns the result is placed in Sess sess.run (init_op) # initialization run Writer = tf.summary.FileWriter ( ' Graph ' , Sess .graph) # generates operation log file, the algorithm can be viewed constructed ===> to view: at the command line D: \ PythonProject \ TensorFlow> tensorboard --logdir = graph, graph Filewriter input parameters will return the URL of replication paste for I in Range (100 ): total_loss =0 for X, Y in ZIP (X_train, Y_train): _, L = sess.run ([Optimizer, Loss], = {X-feed_dict: X, the Y: Y}) # calculated error value total_loss = L + # this is "L" return error accumulates total.append (total_loss / N_SAMPLES) # not explained Print ( ' Epoch {0}: Loss. 1 {} ' .format (I, total_loss / N_SAMPLES)) writer.Close () b_value, w_value = sess.run ([B, W]) y_pred = X_train w_value * + b_value Print ( ' the Done ' ) plt.plot(X_train, Y_train, 'bo',label='Real data') plt.plot(X_train, Y_pred, 'r', label='Predicted Data') plt.legend() plt.show() plt.plot(total) plt.show()