87 lines
3.5 KiB
Python
87 lines
3.5 KiB
Python
# 双y轴曲线图例合并是一个棘手的操作，现以MNIST案例中loss/accuracy绘制曲线。
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import tensorflow as tf
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from tensorflow.examples.tutorials.mnist import input_data
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import time
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import matplotlib.pyplot as plt
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import numpy as np
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x_data = tf.compat.v1.placeholder(tf.float32, [None, 784])
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y_data = tf.compat.v1.placeholder(tf.float32, [None, 10])
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x_image = tf.reshape(x_data, [-1, 28, 28, 1])
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# convolve layer 1
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filter1 = tf.Variable(tf.truncated_normal([5, 5, 1, 6]))
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bias1 = tf.Variable(tf.truncated_normal([6]))
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conv1 = tf.nn.conv2d(x_image, filter1, strides=[1, 1, 1, 1], padding='SAME')
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h_conv1 = tf.nn.sigmoid(conv1 + bias1)
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maxPool2 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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# convolve layer 完整例子
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filter2 = tf.Variable(tf.truncated_normal([5, 5, 6, 16]))
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bias2 = tf.Variable(tf.truncated_normal([16]))
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conv2 = tf.nn.conv2d(maxPool2, filter2, strides=[1, 1, 1, 1], padding='SAME')
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h_conv2 = tf.nn.sigmoid(conv2 + bias2)
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maxPool3 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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# convolve layer 3
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filter3 = tf.Variable(tf.truncated_normal([5, 5, 16, 120]))
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bias3 = tf.Variable(tf.truncated_normal([120]))
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conv3 = tf.nn.conv2d(maxPool3, filter3, strides=[1, 1, 1, 1], padding='SAME')
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h_conv3 = tf.nn.sigmoid(conv3 + bias3)
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# full connection layer 1
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W_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 120, 80]))
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b_fc1 = tf.Variable(tf.truncated_normal([80]))
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h_pool2_flat = tf.reshape(h_conv3, [-1, 7 * 7 * 120])
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h_fc1 = tf.nn.sigmoid(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
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# full connection layer 完整例子
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W_fc2 = tf.Variable(tf.truncated_normal([80, 10]))
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b_fc2 = tf.Variable(tf.truncated_normal([10]))
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y_model = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)
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cross_entropy = - tf.reduce_sum(y_data * tf.log(y_model))
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train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy)
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sess = tf.InteractiveSession()
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correct_prediction = tf.equal(tf.argmax(y_data, 1), tf.argmax(y_model, 1))
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accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
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sess.run(tf.global_variables_initializer())
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mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
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fig_loss = np.zeros([1000])
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fig_accuracy = np.zeros([1000])
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start_time = time.time()
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for i in range(1000):
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batch_xs, batch_ys = mnist.train.next_batch(200)
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if i % 100 == 0:
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train_accuracy = sess.run(accuracy, feed_dict={x_data: batch_xs, y_data: batch_ys})
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print("step %d, train accuracy %g" % (i, train_accuracy))
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end_time = time.time()
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print("time:", (end_time - start_time))
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start_time = end_time
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print("********************************")
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train_step.run(feed_dict={x_data: batch_xs, y_data: batch_ys})
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fig_loss[i] = sess.run(cross_entropy, feed_dict={x_data: batch_xs, y_data: batch_ys})
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fig_accuracy[i] = sess.run(accuracy, feed_dict={x_data: batch_xs, y_data: batch_ys})
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print("test accuracy %g" % sess.run(accuracy, feed_dict={x_data: mnist.test.images, y_data: mnist.test.labels}))
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# 绘制曲线
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fig, ax1 = plt.subplots()
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ax2 = ax1.twinx()
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lns1 = ax1.plot(np.arange(1000), fig_loss, label="Loss")
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# 按一定间隔显示实现方法
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# ax2.plot(200 * np.arange(len(fig_accuracy)), fig_accuracy, 'r')
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lns2 = ax2.plot(np.arange(1000), fig_accuracy, 'r', label="Accuracy")
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ax1.set_xlabel('iteration')
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ax1.set_ylabel('training loss')
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ax2.set_ylabel('training accuracy')
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# 合并图例
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lns = lns1 + lns2
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labels = ["Loss", "Accuracy"]
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# labels = [l.get_label() for l in lns]
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plt.legend(lns, labels, loc=7)
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plt.show() |