TF：基于CNN(2+1)實現(xiàn)MNIST手寫數(shù)字圖片識別準確率提高到99%

導讀
與Softmax回歸模型相比，使用兩層卷積的神經(jīng)網(wǎng)絡(luò)模型借助了卷積的威力，準確率高非常大的提升。

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目錄

輸出結(jié)果

代碼實現(xiàn)

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輸出結(jié)果

Extracting MNIST_data/train-images-idx3-ubyte.gz Extracting MNIST_data/train-labels-idx1-ubyte.gz Extracting MNIST_data/t10k-images-idx3-ubyte.gz Extracting MNIST_data/t10k-labels-idx1-ubyte.gzstep 0, training accuracy 0.1 step 1000, training accuracy 0.98 step 2000, training accuracy 0.96 step 3000, training accuracy 1 step 4000, training accuracy 1 step 5000, training accuracy 0.98 step 6000, training accuracy 0.98 step 7000, training accuracy 1 step 8000, training accuracy 1 step 9000, training accuracy 1 step 10000, training accuracy 1 step 11000, training accuracy 1 step 12000, training accuracy 1 step 13000, training accuracy 0.98 step 14000, training accuracy 1 step 15000, training accuracy 1 step 16000, training accuracy 1 step 17000, training accuracy 1 step 18000, training accuracy 1 step 19000, training accuracy 1

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代碼實現(xiàn)

#TF：基于CNN實現(xiàn)MNIST手寫數(shù)字識別準確率提高到99%import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data……if __name__ == '__main__':mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)x = tf.placeholder(tf.float32, [None, 784])y_ = tf.placeholder(tf.float32, [None, 10])x_image = tf.reshape(x, [-1, 28, 28, 1]) #x_image就是輸入的訓練圖像W_conv1 = weight_variable([5, 5, 1, 32])b_conv1 = bias_variable([32])h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) #是真正進行卷積計算，再選用ReLU作為激活函數(shù)h_pool1 = max_pool_2x2(h_conv1) #調(diào)用函數(shù)max_pool_2x2 進行一次池化操作。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)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)keep_prob = tf.placeholder(tf.float32)h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)W_fc2 = weight_variable([1024, 10])b_fc2 = bias_variable([10])y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))train_step = tf.train.AdamOptimizer(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, tf.float32))sess = tf.InteractiveSession()sess.run(tf.global_variables_initializer())for i in range(20000): # 訓練20000步batch = mnist.train.next_batch(50)# 每100步報告一次在驗證集上的準確度if i % 100 == 0:train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})print("step %d, training accuracy %g" % (i, train_accuracy))train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})print("test accuracy %g" % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

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相關(guān)文章
TF：基于CNN(2+1)實現(xiàn)MNIST手寫數(shù)字識別準確率提高到99%

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