TensorFlow-mnist實際操練(矩陣運算)
阿新 • • 發佈:2019-01-11
TensorFlow-mnist實際操練(矩陣運算,非卷積運算)
硬體:NVIDIA-GTX1080
軟體:Windows7、python3.6.5、tensorflow-gpu-1.4.0
一、基礎知識
1、matmul's different from tf.nn.conv2d
2、None*784 x 784*10 = None*10
二、資料下載
三、程式碼展示
import tensorflow as tf import tensorflow.examples.tutorials.mnist.input_data as input_data def add_layer(inputs, in_size, out_size, activate_function = None): Weights = tf.Variable(tf.random_normal([in_size, out_size])) Biases = tf.Variable(tf.zeros([1,out_size]) + 0.1) #matmul's different from tf.nn.conv2d Wx_plus_b = tf.matmul(inputs, Weights) + Biases #None*784 x 784*10 = None*10 if activate_function is None: outputs = Wx_plus_b else: outputs = activate_function(Wx_plus_b) return outputs def compute_accuracy(v_xs, v_ys): global prediction y_pre = sess.run(prediction, feed_dict = {xs: v_xs}) correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))#ture to 1.0, false to 0.0 result = sess.run(accuracy) return result mnist = input_data.read_data_sets('MNIST_data/', one_hot = True) xs = tf.placeholder(tf.float32, [None, 784]) ys = tf.placeholder(tf.float32, [None, 10]) prediction = add_layer(xs, 784, 10, activate_function = tf.nn.softmax) cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices = [1])) optimizer = tf.train.GradientDescentOptimizer(0.5) train_step = optimizer.minimize(cross_entropy) init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict = {xs: batch_xs, ys: batch_ys}) if i%50 == 0: print(compute_accuracy(mnist.test.images, mnist.test.labels)) sess.close()
四、結果展示
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.gz 0.1004 0.6287 0.7282 0.7737 0.7929 0.8175 0.8315 0.8372 0.8456 0.8529 0.8572 0.8594 0.8607 0.8677 0.8613 0.8678 0.8731 0.8748 0.8752 0.8777
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