Minist數據集：MNIST_data 包含四個數據文件
一、方法一：經典方法 tf.matmul(X,w)+b
import tensorflow as tf import numpy as np import input_data import time #define paramaters learning_rate=0.01 batch_size=128 n_epochs=900 # 1.read from data file #using TF learn built in function to load MNIST data to the folder data mnist=input_data.read_data_sets('MNIST_data/',one_hot=True) # 2.creat placeholders for features and label # each img in mnist data is 28*28 ,therefor need a 1*784 tensor # 10 classes corresponding to 0-9 X=tf.placeholder(tf.float32,[batch_size,784],name='X_placeholder') Y=tf.placeholder(tf.float32,[batch_size,10 ],name='Y_placeholder') # 3.creat weight and bias ,w init to random variables with mean of 0 ; # b init to 0 ,shape of b depends on Y ,shape of w depends on the dimension of X and Y_placeholder w=tf.Variable(tf.random_normal(shape=[784,10],stddev=0.01),name='weights') b=tf.Variable(tf.zeros([1,10]),name="bias") # 4.build model to predict # the model that returns the logits ,the logits will later passed through softmax layer logits=tf.matmul(X,w)+b # 5.define lose function # use cross entropy of softmax of logits as the loss function entropy=tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y,name='loss') loss=tf.reduce_mean(entropy) # 6.define training open # using gradient descent with learning rate of 0.01 to minimize loss optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) with tf.Session() as sess: writer=tf.summary.FileWriter('./my_graph/logistic_reg',sess.graph) start_time= time.time() sess.run(tf.global_variables_initializer()) n_batches=int(mnist.train.num_examples/batch_size) for i in range(n_epochs) : #train n_epochs times total_loss=0 for _ in range(n_batches): X_batch,Y_batch=mnist.train.next_batch(batch_size) _,loss_batch=sess.run([optimizer,loss],feed_dict={X:X_batch,Y:Y_batch}) total_loss +=loss_batch if i%100==0: print('Average loss epoch {0} : {1}'.format(i,total_loss/n_batches)) print('Total time: {0} seconds'.format(time.time()-start_time)) print('Optimization Finished!') # 7.test the model n_batches=int(mnist.test.num_examples/batch_size) total_correct_preds=0 for i in range(n_batches): X_batch,Y_batch=mnist.test.next_batch(batch_size) _,loss_batch,logits_batch=sess.run([optimizer,loss,logits],feed_dict={X:X_batch,Y:Y_batch}) preds=tf.nn.softmax(logits_batch) correct_preds=tf.equal(tf.argmax(preds,1),tf.argmax(Y_batch,1)) accuracy=tf.reduce_sum(tf.cast(correct_preds,tf.float32)) total_correct_preds+=sess.run(accuracy) print('Accuracy {0}'.format(total_correct_preds/mnist.test.num_examples)) writer.close()
准確率大約是92%，TFboard：
二、方法二：deep learning 卷積神經網絡
# load MNIST data import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # start tensorflow interactiveSession import tensorflow as tf sess = tf.InteractiveSession() # weight initialization def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape = shape) return tf.Variable(initial) # convolution def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') # pooling def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # Create the model # placeholder x = tf.placeholder("float", [None, 784]) y_ = tf.placeholder("float", [None, 10]) # variables W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x,W) + b) print (y) # first convolutinal layer w_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) print (x) x_image = tf.reshape(x, [-1, 28, 28, 1]) print (x_image) h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) print (h_conv1) print (h_pool1) # second convolutional layer w_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) print (h_conv2) print (h_pool2) # densely connected layer w_fc1 = weight_variable([7*7*64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1) print (h_fc1) # dropout keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) print (h_fc1_drop) # readout layer w_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2) # train and evaluate the model cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy) #train_step = tf.train.AdagradOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) sess.run(tf.global_variables_initializer()) writer=tf.summary.FileWriter('./my_graph/mnist_deep',sess.graph) # Train tf.initialize_all_variables().run() for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) #print (batch_xs.shape,batch_ys) if i % 100 == 0: train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys, keep_prob:0.5}) print (("step %d, train accuracy %g" % (i, train_accuracy))) train_step.run({x: batch_xs, y_: batch_ys, keep_prob:0.5}) #print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels})) # Test trained model print( ("python_base accuracy %g" % accuracy.eval(feed_dict={x:mnist.test.images[0:500], y_:mnist.test.labels[0:500], keep_prob:0.5}))) writer.close()
准確率達到98%，Board：
三、第三種 使用minist數據集做圖像去噪
from keras.datasets import mnist from keras.layers import Input, Dense from keras.models import Model from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D import numpy as np from keras.callbacks import TensorBoard import matplotlib.pyplot as plt (x_train, _), (x_test, _) = mnist.load_data() x_train = x_train.astype('float32') / 255. x_test = x_test.astype('float32') / 255. x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) # adapt this if using `channels_first` image data format x_test = np.reshape(x_test, (len(x_test), 28, 28, 1)) # adapt this if using `channels_first` image data format noise_factor = 0.5 x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) x_train_noisy = np.clip(x_train_noisy, 0., 1.) x_test_noisy = np.clip(x_test_noisy, 0., 1.) x_train_noisy = x_train_noisy.astype(np.float) x_test_noisy = x_test_noisy.astype(np.float) input_img = Input(shape=(28, 28, 1)) # adapt this if using `channels_first` image data format x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img) x = MaxPooling2D((2, 2), padding='same')(x) x = Conv2D(32, (3, 3), activation='relu', padding='same')(x) encoded = MaxPooling2D((2, 2), padding='same')(x) # at this point the representation is (7, 7, 32) x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded) x = UpSampling2D((2, 2))(x) x = Conv2D(32, (3, 3), activation='relu', padding='same')(x) x = UpSampling2D((2, 2))(x) decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x) autoencoder = Model(input_img, decoded) autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy') autoencoder.fit(x_train_noisy, x_train, epochs=100, batch_size=128, shuffle=True, validation_data=(x_test_noisy, x_test), callbacks=[TensorBoard(log_dir='/tmp/tb', histogram_freq=0, write_graph=True)]) n = 10 plt.figure(figsize=(20, 4)) for i in range(n): #noisy data ax = plt.subplot(3, n, i+1) plt.imshow(x_test_noisy[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) #predict ax = plt.subplot(3, n, i+1+n) decoded_img = autoencoder.predict(x_test_noisy) plt.imshow(decoded_img[i].reshape(28, 28)) plt.gray() ax.get_yaxis().set_visible(False) ax.get_xaxis().set_visible(False) #original ax = plt.subplot(3, n, i+1+2*n) plt.imshow(x_test[i].reshape(28, 28)) plt.gray() ax.get_yaxis().set_visible(False) ax.get_xaxis().set_visible(False) plt.show()
使用了keras，見官網 https://blog.keras.io/building-autoencoders-in-keras.html
第一行是加了噪聲的圖，第二行是去噪以后的圖，第三行是原圖，回復效果較好
125s跑一個epoch，100組三個半小時搞定
tensorboard --logdir=/tmp/tb