Add 04 homework

04. recognizing-handwritten-digits/main.py

+# -*- coding: utf-8 -*-
+
+import numpy as np
+import tensorflow as tf
+from mnist import input_data
+import matplotlib.pyplot as plt
+
+def load_data():
+    mnist = input_data.read_data_sets('data', one_hot=True)
+    return mnist
+
+def model(x):
+    with tf.name_scope("weights"):
+        W = tf.Variable(tf.random.normal([784, 10]), name="W")
+        
+    with tf.name_scope("biases"):
+        b = tf.Variable(tf.zeros([10]), name="b")
+        
+    with tf.name_scope("neural"):
+        forward = tf.matmul(x, W) + b  #don't use multiply or *
+    
+    with tf.name_scope("softmax"):
+        pred = tf.nn.softmax(forward)
+    
+    return pred
+
+def train(mnist):
+    #tensorboard settings
+    tf.compat.v1.reset_default_graph()    #reset compution graoh
+    logdir = "logs"
+    
+    #training hyperparameters
+    train_epochs = 150
+    learning_rate = 0.025
+    batch_size = 100
+    
+    #other parameters
+    loss_list = []
+    display_step = 1
+    total_batch = int(mnist.train.num_examples/batch_size)  #how many batches in 1 epoch
+    
+    with tf.name_scope("Input"):
+        tf.compat.v1.disable_eager_execution()
+        x = tf.compat.v1.placeholder(tf.float32, [None, 784], name="X")    #'None' help to use mini-batch gradient descent
+        y = tf.compat.v1.placeholder(tf.float32, [None, 10], name="Y")
+    
+    with tf.name_scope("Model"):
+        pred = model(x)
+        
+    with tf.name_scope("LossFunction"):
+        loss_function = tf.reduce_mean(-tf.compat.v1.reduce_sum(y*tf.math.log(pred), reduction_indices=1))     #cross entropy loss
+        #write loss as scalar to tensorboard
+        tf.summary.scalar("loss", loss_function)
+        
+    with tf.name_scope("Optimizer"):
+        optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate).minimize(loss_function)
+        
+    with tf.name_scope("Accuracy"):
+        correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))    #argmax(-, 1) - return the index of the max number in each column
+        accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))    #tf.cast(-, tf.float32) - transform the type to tf.float32
+        
+    init = tf.compat.v1.global_variables_initializer()
+    
+    merged = tf.compat.v1.summary.merge_all()     #合并需要记录的文件，方便一次性写入
+    
+    with tf.compat.v1.Session() as sess:
+        sess.run(init)
+        
+        #create writer for writing the computation graph to the log
+        writer = tf.compat.v1.summary.FileWriter(logdir, sess.graph)
+        
+        for epoch in range(train_epochs):
+            for batch in range(total_batch):
+                #next_batch() is made for training the model, so it will shuffle the data inside automatically
+                #   and read the next batch data when it is run again.
+                batch_images_xs, batch_labels_ys = mnist.train.next_batch(batch_size=batch_size)
+                
+                sess.run(optimizer, feed_dict={x:batch_images_xs, y:batch_labels_ys})
+                
+                #writer.add_summary(summary, epoch)  #After 1 epoch, 将运行在val set的所有summary写入文件
+                
+            #After 1 epoch, compute loss and accuracy on validation set. Validation set is not batched.
+            loss, acc = sess.run([loss_function, accuracy], feed_dict={x:mnist.validation.images, y:mnist.validation.labels})
+            loss_list.append(loss)
+            
+            #print loss and accuracy
+            if (epoch+1) % display_step == 0:
+                print("epoch=%02d" % (epoch+1), "loss=", "{:.9f}".format(loss), "accuracy=", "{:.4f}".format(acc))
+            
+        print("Train Finished.")
+        
+        #Test the model on training set
+        acc_test = sess.run(accuracy, feed_dict={x:mnist.train.images, y:mnist.train.labels})
+        print("Training set Accuracy=", acc_test)
+        
+        #Test the model on test set
+        pred_show, acc_test = sess.run([tf.argmax(pred, 1), accuracy], feed_dict={x:mnist.test.images, y:mnist.test.labels})
+        print("Test set Accuracy=", acc_test)
+        
+    #output loss curve
+    fig1 = plt.figure()
+    plt.plot(loss_list)
+    fig1.savefig('loss.png')
+            
+    writer.close()
+    
+    return pred_show
+
+def visualize_prediction(images,        #images list
+                         labels,        #labels list
+                         pred_show,     #pred_show list
+                         index,         #start visualizing the prediction from the 'index'th image
+                         num=10):       #display 'num=10' images at a time
+    fig = plt.gcf()     #获取当前图表(get current figure)
+    fig.set_size_inches(10, 12)     #单位-英寸，1英寸=2.54cm
+    if num > 25:
+        num = 25    #最多显示25个子图
+        
+    for i in range(0, num):
+        ax = plt.subplot(5, 5, i+1)     #获取当前要处理的子图
+        ax.imshow(np.reshape(images[index], (28, 28)), cmap="binary")   #显示第index个image
+        title = "label=" + str(np.argmax(labels[index]))    #构建该图要显示的title
+        if len(pred_show) > 0:     #若pred_show不为空，则显示
+            title += ", predict=" + str(pred_show[index])
+        
+        ax.set_title(title, fontsize=10)    #显示图上的title信息
+        ax.set_xticks([])   #不显示坐标轴
+        ax.set_yticks([])
+        index += 1
+         
+    fig.savefig('prediction.png')
+
+def main(argv=None):
+    mnist = load_data()    #load data
+    pred_show = train(mnist)
+    visualize_prediction(mnist.test.images, mnist.test.labels, pred_show, 10, 25)
+
+if __name__=='__main__':
+    tf.compat.v1.app.run()