邏輯斯特回歸tensorflow實現
阿新 • • 發佈:2018-08-20
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#!/usr/bin/python2.7 #coding:utf-8 from __future__ import print_function import tensorflow as tf # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("../Mnist_data/", one_hot=True) print(mnist) # Parameters setting learning_rate = 0.01 training_epochs = 25 # 訓練叠代的次數 batch_size = 100 # 一次輸入的樣本 display_step = 1 # set the tf Graph Input & set the model weights x = tf.placeholder(dtype=tf.float32, shape=[None,784], name="input_x") y = tf.placeholder(dtype=tf.float32, shape=[None,10], name="input_y") #set models weights,bias W=tf.Variable(tf.zeros([784,10])) b=tf.Variable(tf.zeros([10])) # Construct the model pred=tf.nn.softmax(tf.matmul(x,W)+b) # 歸一化,the possibility of getting the right value # Minimize error using cross entropy & set the gradient descent cost=tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred),reduction_indices=1)) #交叉熵,reducion_indices=1橫向求和 optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # Start training with tf.Session() as sess: # Run the initializer sess.run(init) # Training cycle for epoch in range(training_epochs): avg_cost = 0. total_batch = int(mnist.train.num_examples/batch_size) # Loop over all batches for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, y: batch_ys}) # Compute average loss avg_cost += c / total_batch # Display logs per epoch step if (epoch+1) % display_step == 0: print("Epoch:", ‘%04d‘ % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) print("Optimization Finished!") # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
邏輯斯特回歸tensorflow實現