tensorflow tutorials：卷积神经网络可视化
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CONVOLUTIONAL NEURAL NETWORK
In [1]:import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data %matplotlib inline print ("PACKAGES LOADED")
PACKAGES LOADED
LOAD MNIST DATA
In [2]:mnist = input_data.read_data_sets('/tmp/data/', one_hot=True) train_X = mnist.train.images train_Y = mnist.train.labels test_X = mnist.test.images test_Y = mnist.test.labels print ("MNIST ready")
Extracting /tmp/data/train-images-idx3-ubyte.gz Extracting /tmp/data/train-labels-idx1-ubyte.gz Extracting /tmp/data/t10k-images-idx3-ubyte.gz Extracting /tmp/data/t10k-labels-idx1-ubyte.gz MNIST ready
SELECT DEVICE TO BE USED
In [3]:device_type = "/cpu:1"
DEFINE CNN
In [4]:with tf.device(device_type): # <= This is optional n_input = 784 n_output = 10 weights = 'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64], stddev=0.1)), 'wd1': tf.Variable(tf.random_normal([14*14*64, n_output], stddev=0.1)) biases = 'bc1': tf.Variable(tf.random_normal([64], stddev=0.1)), 'bd1': tf.Variable(tf.random_normal([n_output], stddev=0.1)) def conv_model(_input, _w, _b): # Reshape input _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1]) # Convolution _conv1 = tf.nn.conv2d(_input_r, _w['wc1'], strides=[1, 1, 1, 1], padding='SAME') # Add-bias _conv2 = tf.nn.bias_add(_conv1, _b['bc1']) # Pass ReLu _conv3 = tf.nn.relu(_conv2) # Max-pooling _pool = tf.nn.max_pool(_conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # Vectorize _dense = tf.reshape(_pool, [-1, _w['wd1'].get_shape().as_list()[0]]) # Fully-connected layer _out = tf.add(tf.matmul(_dense, _w['wd1']), _b['bd1']) # Return everything out = 'input_r': _input_r, 'conv1': _conv1, 'conv2': _conv2, 'conv3': _conv3 , 'pool': _pool, 'dense': _dense, 'out': _out return out print ("CNN ready")
CNN ready
DEFINE COMPUTATIONAL GRAPH
In [5]:# tf Graph input x = tf.placeholder(tf.float32, [None, n_input]) y = tf.placeholder(tf.float32, [None, n_output]) # Parameters learning_rate = 0.001 training_epochs = 10 batch_size = 100 display_step = 1 # Functions! with tf.device(device_type): # <= This is optional prediction = conv_model(x, weights, biases)['out'] cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(prediction, y)) optm = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) corr = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1)) # Count corrects accr = tf.reduce_mean(tf.cast(corr, tf.float32)) # Accuracy init = tf.initialize_all_variables() # Saver save_step = 1; savedir = "tmp/" saver = tf.train.Saver(max_to_keep=3) print ("Network Ready to Go!")
Network Ready to Go!
OPTIMIZE
DO TRAIN OR NOT
In [6]:do_train = 1 sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) sess.run(init)In [7]:
if do_train == 1: 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) # Fit training using batch data sess.run(optm, feed_dict=x: batch_xs, y: batch_ys) # Compute average loss avg_cost += sess.run(cost, feed_dict=x: batch_xs, y: batch_ys)/total_batch # Display logs per epoch step if epoch % display_step == 0: print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost)) train_acc = sess.run(accr, feed_dict=x: batch_xs, y: batch_ys) print (" Training accuracy: %.3f" % (train_acc)) test_acc = sess.run(accr, feed_dict=x: test_X, y: test_Y) print (" Test accuracy: %.3f" % (test_acc)) # Save Net #if epoch % save_step == 0: # saver.save(sess, "nets/cnn_mnist_simple.ckpt-" + str(epoch)) print ("Optimization Finished.")
Epoch: 000/010 cost: 0.335752019 Training accuracy: 0.990 Test accuracy: 0.961 Epoch: 001/010 cost: 0.102068414 Training accuracy: 0.990 Test accuracy: 0.975 Epoch: 002/010 cost: 0.070511335 Training accuracy: 0.990 Test accuracy: 0.979 Epoch: 003/010 cost: 0.056912913 Training accuracy: 0.980 Test accuracy: 0.979 Epoch: 004/010 cost: 0.048582980 Training accuracy: 0.990 Test accuracy: 0.981 Epoch: 005/010 cost: 0.041515299 Training accuracy: 0.970 Test accuracy: 0.982 Epoch: 006/010 cost: 0.036498022 Training accuracy: 0.980 Test accuracy: 0.983 Epoch: 007/010 cost: 0.031456893 Training accuracy: 0.980 Test accuracy: 0.981 Epoch: 008/010 cost: 0.028272405 Training accuracy: 1.000 Test accuracy: 0.983 Epoch: 009/010 cost: 0.025033342 Training accuracy: 1.000 Test accuracy: 0.982 Optimization Finished.
RESTORE
In [8]:if do_train == 0: epoch = training_epochs-1 saver.restore(sess, "/tmp/cnn_mnist_simple.ckpt-" + str(epoch)) print ("NETWORK RESTORED")
LET'S SEE HOW CNN WORKS
In [11]:with tf.device(device_type): conv_out = conv_model(x, weights, biases) input_r = sess.run(conv_out['input_r'], feed_dict=x: train_X[0:1, :]) conv1 = sess.run(conv_out['conv1'], feed_dict=x: train_X[0:1, :]) conv2 = sess.run(conv_out['conv2'], feed_dict=x: train_X[0:1, :]) conv3 = sess.run(conv_out['conv3'], feed_dict=x: train_X[0:1, :]) pool = sess.run(conv_out['pool'], feed_dict=x: train_X[0:1, :]) dense = sess.run(conv_out['dense'], feed_dict=x: train_X[0:1, :]) out = sess.run(conv_out['out'], feed_dict=x: train_X[0:1, :])
Input
In [12]:# Let's see 'input_r' print ("Size of 'input_r' is %s" % (input_r.shape,)) label = np.argmax(train_Y[0, :]) print ("Label is %d" % (label)) # Plot ! plt.matshow(input_r[0, :, :, 0], cmap=plt.get_cmap('gray')) plt.title("Label of this image is " + str(label) + "") plt.colorbar() plt.show()
Size of 'input_r' is (1, 28, 28, 1) Label is 7
Conv1 (convolution)
In [13]:# Let's see 'conv1' print ("Size of 'conv1' is %s" % (conv1.shape,)) # Plot ! for i in range(3): plt.matshow(conv1[0, :, :, i], cmap=plt.get_cmap('gray')) plt.title(str(i) + "th conv1") plt.colorbar() plt.show()
Size of 'conv1' is (1, 28, 28, 64)
Conv2 (+bias)
In [14]:# Let's see 'conv2' print ("Size of 'conv2' is %s" % (conv2.shape,)) # Plot ! for i in range(3): plt.matshow(conv2[0, :, :, i], cmap=以上是关于tensorflow tutorials：卷积神经网络可视化的主要内容，如果未能解决你的问题，请参考以下文章学习笔记TF057:TensorFlow MNIST，卷积神经网络循环神经网络无监督学习