cnn是什么意思中文翻译（怎么一边翻译一边看cnn）

MNIST数据集

我们的卷积神经网络模型将似于LeNet-5架构，由卷积层、最大池化和非线性操作层。

cnn是什么意思中文翻译,怎么一边翻译一边看cnn(29)

卷积神经网络三维仿真

代码：

# Import the deep learning library import tensorflow as tf import time # Import the MNIST dataset from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) # Network inputs and outputs # The network's input is a 28×28 dimensional input n = 28 m = 28 num_input = n * m # MNIST data input num_classes = 10 # MNIST total classes (0-9 digits) # tf Graph input X = tf.placeholder(tf.float32, [None, num_input]) Y = tf.placeholder(tf.float32, [None, num_classes]) # Storing the parameters of our LeNET-5 inspired Convolutional Neural Network weights = { "W_ij": tf.Variable(tf.random_normal([5, 5, 1, 32])), "W_jk": tf.Variable(tf.random_normal([5, 5, 32, 64])), "W_kl": tf.Variable(tf.random_normal([7 * 7 * 64, 1024])), "W_lm": tf.Variable(tf.random_normal([1024, num_classes])) } biases = { "b_ij": tf.Variable(tf.random_normal([32])), "b_jk": tf.Variable(tf.random_normal([64])), "b_kl": tf.Variable(tf.random_normal([1024])), "b_lm": tf.Variable(tf.random_normal([num_classes])) } # The hyper-parameters of our Convolutional Neural Network learning_rate = 1e-3 num_steps = 500 batch_size = 128 display_step = 10 def ConvolutionLayer(x, W, b, strides=1): # Convolution Layer x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') x = tf.nn.bias_add(x, b) return x def ReLU(x): # ReLU activation function return tf.nn.relu(x) def PoolingLayer(x, k=2, strides=2): # Max Pooling layer return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, strides, strides, 1], padding='SAME') def Softmax(x): # Softmax activation function for the CNN's final output return tf.nn.softmax(x) # Create model def ConvolutionalNeuralNetwork(x, weights, biases): # MNIST data input is a 1-D row vector of 784 features (28×28 pixels) # Reshape to match picture format [Height x Width x Channel] # Tensor input become 4-D: [Batch Size, Height, Width, Channel] x = tf.reshape(x, shape=[-1, 28, 28, 1]) # Convolution Layer Conv1 = ConvolutionLayer(x, weights["W_ij"], biases["b_ij"]) # Non-Linearity ReLU1 = ReLU(Conv1) # Max Pooling (down-sampling) Pool1 = PoolingLayer(ReLU1, k=2) # Convolution Layer Conv2 = ConvolutionLayer(Pool1, weights["W_jk"], biases["b_jk"]) # Non-Linearity ReLU2 = ReLU(Conv2) # Max Pooling (down-sampling) Pool2 = PoolingLayer(ReLU2, k=2) # Fully connected layer # Reshape conv2 output to fit fully connected layer input FC = tf.reshape(Pool2, [-1, weights["W_kl"].get_shape().as_list()[0]]) FC = tf.add(tf.matmul(FC, weights["W_kl"]), biases["b_kl"]) FC = ReLU(FC) # Output, class prediction output = tf.add(tf.matmul(FC, weights["W_lm"]), biases["b_lm"]) return output # Construct model logits = ConvolutionalNeuralNetwork(X, weights, biases) prediction = Softmax(logits) # Softamx cross entropy loss function loss_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=Y)) # Optimization using the Adam Gradient Descent optimizer optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) training_process = optimizer.minimize(loss_function) # Evaluate model correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # recording how the loss functio varies over time during training cost = tf.summary.scalar("cost", loss_function) training_accuracy = tf.summary.scalar("accuracy", accuracy) train_summary_op = tf.summary.merge([cost,training_accuracy]) train_writer = tf.summary.FileWriter("./Desktop/logs", graph=tf.get_default_graph()) # 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) start_time = time.time() for step in range(1, num_steps 1): batch_x, batch_y = mnist.train.next_batch(batch_size) # Run optimization op (backprop) sess.run(training_process, feed_dict={X: batch_x, Y: batch_y}) if step % display_step == 0 or step == 1: # Calculate batch loss and accuracy loss, acc, summary = sess.run([loss_function, accuracy, train_summary_op], feed_dict={X: batch_x, Y: batch_y}) train_writer.add_summary(summary, step) print("Step " str(step) ", Minibatch Loss= " \ "{:.4f}".format(loss) ", Training Accuracy= " \ "{:.3f}".format(acc)) end_time = time.time() print("Time duration: " str(int(end_time-start_time)) " seconds") print("Optimization Finished!") # Calculate accuracy for 256 MNIST test images print("Testing Accuracy:", \ sess.run(accuracy, feed_dict={X: mnist.test.images[:256], Y: mnist.test.labels[:256]}))

上面的代码显得有些冗长，但如果一段一段的对其进行分解，读起来不是很难理解。

运行完该程序，对应结果应如下所示：

Step 1, Minibatch Loss= 74470.4844, Training Accuracy= 0.117 Step 10, Minibatch Loss= 20529.4141, Training Accuracy= 0.250 Step 20, Minibatch Loss= 14074.7539, Training Accuracy= 0.531 Step 30, Minibatch Loss= 7168.9839, Training Accuracy= 0.586 Step 40, Minibatch Loss= 4781.1060, Training Accuracy= 0.703 Step 50, Minibatch Loss= 3281.0979, Training Accuracy= 0.766 Step 60, Minibatch Loss= 2701.2451, Training Accuracy= 0.781 Step 70, Minibatch Loss= 2478.7153, Training Accuracy= 0.773 Step 80, Minibatch Loss= 2312.8320, Training Accuracy= 0.820 Step 90, Minibatch Loss= 2143.0774, Training Accuracy= 0.852 Step 100, Minibatch Loss= 1373.9169, Training Accuracy= 0.852 Step 110, Minibatch Loss= 1852.9535, Training Accuracy= 0.852 Step 120, Minibatch Loss= 1845.3500, Training Accuracy= 0.891 Step 130, Minibatch Loss= 1677.2566, Training Accuracy= 0.844 Step 140, Minibatch Loss= 1683.3661, Training Accuracy= 0.875 Step 150, Minibatch Loss= 1859.3821, Training Accuracy= 0.836 Step 160, Minibatch Loss= 1495.4796, Training Accuracy= 0.859 Step 170, Minibatch Loss= 609.3800, Training Accuracy= 0.914 Step 180, Minibatch Loss= 1376.5054, Training Accuracy= 0.891 Step 190, Minibatch Loss= 1085.0363, Training Accuracy= 0.891 Step 200, Minibatch Loss= 1129.7145, Training Accuracy= 0.914 Step 210, Minibatch Loss= 1488.5452, Training Accuracy= 0.906 Step 220, Minibatch Loss= 584.5027, Training Accuracy= 0.930 Step 230, Minibatch Loss= 619.9744, Training Accuracy= 0.914 Step 240, Minibatch Loss= 1575.8933, Training Accuracy= 0.891 Step 250, Minibatch Loss= 1558.5853, Training Accuracy= 0.891 Step 260, Minibatch Loss= 375.0371, Training Accuracy= 0.922 Step 270, Minibatch Loss= 1568.0758, Training Accuracy= 0.859 Step 280, Minibatch Loss= 1172.9205, Training Accuracy= 0.914 Step 290, Minibatch Loss= 1023.5415, Training Accuracy= 0.914 Step 300, Minibatch Loss= 475.9756, Training Accuracy= 0.945 Step 310, Minibatch Loss= 488.8930, Training Accuracy= 0.961 Step 320, Minibatch Loss= 1105.7720, Training Accuracy= 0.914 Step 330, Minibatch Loss= 1111.8589, Training Accuracy= 0.906 Step 340, Minibatch Loss= 842.7805, Training Accuracy= 0.930 Step 350, Minibatch Loss= 1514.0153, Training Accuracy= 0.914 Step 360, Minibatch Loss= 1722.1812, Training Accuracy= 0.875 Step 370, Minibatch Loss= 681.6041, Training Accuracy= 0.891 Step 380, Minibatch Loss= 902.8599, Training Accuracy= 0.930 Step 390, Minibatch Loss= 714.1541, Training Accuracy= 0.930 Step 400, Minibatch Loss= 1654.8883, Training Accuracy= 0.914 Step 410, Minibatch Loss= 696.6915, Training Accuracy= 0.906 Step 420, Minibatch Loss= 536.7183, Training Accuracy= 0.914 Step 430, Minibatch Loss= 1405.9148, Training Accuracy= 0.891 Step 440, Minibatch Loss= 199.4781, Training Accuracy= 0.953 Step 450, Minibatch Loss= 438.3784, Training Accuracy= 0.938 Step 460, Minibatch Loss= 409.6419, Training Accuracy= 0.969 Step 470, Minibatch Loss= 503.1216, Training Accuracy= 0.930 Step 480, Minibatch Loss= 482.6476, Training Accuracy= 0.922 Step 490, Minibatch Loss= 767.3893, Training Accuracy= 0.922 Step 500, Minibatch Loss= 626.8249, Training Accuracy= 0.930 Time duration: 657 seconds Optimization Finished! Testing Accuracy: 0.9453125

综上，们刚刚完成了第一个卷积神经网络的构建，正如在上面的结果中所看到的那样，从第一步到最后一步，模型的准确性已经得到很大的提升，但我们的卷积神经网络还有较大的改进空间。

现在让我们在Tensorboard中可视化构建的卷积神经网络模型：

cnn是什么意思中文翻译,怎么一边翻译一边看cnn(30)