【tensorflow 全连接神经网络】 minist 手写数字识别

本文主要是介绍【tensorflow 全连接神经网络】 minist 手写数字识别，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

主要内容：
使用tensorflow构建一个三层全连接传统神经网络,作为字符识别的多分类器。通过字符图片预测对应的数字，对mnist数据集进行预测。

# coding: utf-8
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import mathmnist = input_data.read_data_sets("./mnist/",one_hot=True)print("Training set:",mnist.train.images.shape)
print("Training set labels:",mnist.train.labels.shape)
print("Dev Set(Cross Validation set):",mnist.validation.images.shape)
print("Dev Set labels:",mnist.validation.labels.shape)
print("Test Set:",mnist.test.images.shape)
print("Test Set labels:",mnist.test.labels.shape)x_train = mnist.train.images
y_train = mnist.train.labels
x_dev = mnist.validation.images
y_dev = mnist.validation.labels
x_test = mnist.test.images
y_test = mnist.test.labelsdef display_digit(index):print(y_train[index])label = y_train[index].argmax(axis=0)image = x_train[index].reshape([28,28])plt.title("Example: %d  Label: %d" % (index, label))plt.imshow(image, cmap=plt.get_cmap("gray_r"))plt.show()display_digit(5)
print(y_train[5].shape)#按照Andrew的建议把样本横向排列
x_train = x_train.T
y_train = y_train.T
x_dev = x_dev.T
y_dev = y_dev.T
x_test = x_test.T
y_test = y_test.T
print("x_train shape",x_train.shape)
print("y_train shape",y_train.shape)def random_mini_batches(X,Y,mini_batch_size=64):"""Creates a list of random minibatches from (X, Y)Arguments:X -- input data, of shape (input size, number of examples)Y -- true "label" vector (1 for blue dot / 0 for red dot), of shape (1, number of examples)mini_batch_size -- size of the mini-batches, integerReturns:mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)"""m = X.shape[1] #训练样本个数mini_batches = []# Step 1: Shuffle (X, Y)permutation = list(np.random.permutation(m))shuffled_X = X[:, permutation]shuffled_Y = Y[:, permutation].reshape((-1, m))# Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.num_complete_minibatches = math.floor(m / mini_batch_size)  # number of mini batches of size mini_batch_size in your partitionningfor k in range(0, num_complete_minibatches):mini_batch_X = shuffled_X[:, k * mini_batch_size:(k + 1) * mini_batch_size]mini_batch_Y = shuffled_Y[:, k * mini_batch_size:(k + 1) * mini_batch_size]mini_batch = (mini_batch_X, mini_batch_Y)mini_batches.append(mini_batch)# Handling the end case (last mini-batch < mini_batch_size)if m % mini_batch_size != 0:mini_batch_X = shuffled_X[:, mini_batch_size * num_complete_minibatches:]mini_batch_Y = shuffled_Y[:, mini_batch_size * num_complete_minibatches:]mini_batch = (mini_batch_X, mini_batch_Y)mini_batches.append(mini_batch)return mini_batches"参数初始化"
layer_dims = [784,64,128,10] #三层网络，hidden units个数为64,128，10   一共有10个类别  def init_parameters(layer_dims):parameters = {}L = len(layer_dims) - 1 # number of layers in the networkfor l in range(1,L+1):parameters["W"+str(l)] = tf.Variable(tf.random_normal([layer_dims[l], layer_dims[l-1]]))parameters["b"+str(l)] = tf.Variable(tf.random_normal([layer_dims[l],1]))return parameters    def forward_propagation(X, parameters):W1 = parameters['W1']b1 = parameters['b1']W2 = parameters['W2']b2 = parameters['b2']W3 = parameters['W3']b3 = parameters['b3']Z1 = tf.add(tf.matmul(W1, X), b1)  # Z1 = np.dot(W1, X) + b1A1 = tf.nn.relu(Z1)  # A1 = relu(Z1)Z2 = tf.add(tf.matmul(W2, A1), b2)  # Z2 = np.dot(W2, a1) + b2A2 = tf.nn.relu(Z2)  # A2 = relu(Z2)Z3 = tf.add(tf.matmul(W3, A2), b3)  # Z3 = np.dot(W3,Z2) + b3return Z3def compute_cost(Z3, Y):"""Computes the costArguments:Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (10, number of examples)Y -- "true" labels vector placeholder, same shape as Z3Returns:cost - Tensor of the cost function"""# to fit the tensorflow requirement for tf.nn.softmax_cross_entropy_with_logits(...,...)logits = tf.transpose(Z3)labels = tf.transpose(Y)cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))return costdef tf_nn_model(X_train,Y_train,X_test,Y_test,layer_dims,learning_rate=0.001,num_epochs=100,minibatch_size=64,print_cost=True):(n_x,m) = X_train.shape # (n_x: input size, m : number of examples in the train set)n_y = Y_train.shape[0] # n_y : output sizecosts = [] # to keep track of the costX = tf.placeholder(tf.float32, [n_x, None], name="X")Y = tf.placeholder(tf.float32, [n_y, None], name="Y")parameters = init_parameters(layer_dims)Z3 = forward_propagation(X, parameters)cost = compute_cost(Z3, Y)optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)init  = tf.global_variables_initializer()with tf.Session() as sess:sess.run(init)for epoch in range(num_epochs):epoch_cost = 0.  # Defines a cost related to an epochnum_minibatches = int(m / minibatch_size)minibatches = random_mini_batches(X_train, Y_train, minibatch_size)for minibatch in minibatches:(minibatch_X, minibatch_Y) = minibatch_, minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})epoch_cost += minibatch_cost / num_minibatchesif print_cost == True and epoch % 10 == 0:print("Cost after epoch %i: %f" % (epoch, epoch_cost))if print_cost == True and epoch % 5 == 0:costs.append(epoch_cost)   # plot the costplt.plot(np.squeeze(costs))plt.ylabel('cost')plt.xlabel('iterations (per tens)')plt.title("Learning rate =" + str(learning_rate))plt.show() parameters = sess.run(parameters)print("Parameters have been trained!")correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))print("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train}))print("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))return parameterstf_nn_model(x_train,y_train,x_test,y_test,layer_dims,learning_rate=0.001,num_epochs=100,minibatch_size=64,print_cost=True)

运行结果：

Cost after epoch 0: 75.913229
Cost after epoch 10: 1.541095
Cost after epoch 20: 0.436585
Cost after epoch 30: 0.174160
Cost after epoch 40: 0.090298
Cost after epoch 50: 0.064457
Cost after epoch 60: 0.044082
Cost after epoch 70: 0.035504
Cost after epoch 80: 0.022698
Cost after epoch 90: 0.023649Parameters have been trained!
Train Accuracy: 0.994545
Test Accuracy: 0.9427
Out[106]:
{'W1': array([[ 0.2372188 ,  1.27198195, -0.6455391 , ...,  1.26290512,-0.69059598,  0.36647785],[-0.50644702, -0.74370074,  0.38941762, ..., -0.15578361,-0.31009915, -0.17434931],[-2.5437634 ,  0.44527429, -0.70932513, ..., -1.01713133,-0.14752612,  0.19787782],..., [ 3.25048923,  0.08093037,  0.77567875, ..., -0.79534328,1.43014407,  0.21873565],[-1.93292856, -0.19783179,  0.12327723, ..., -0.22539552,0.13556184,  0.87210643],[-0.93210453,  0.2583403 ,  1.58626533, ..., -1.69557643,0.31096032,  0.41782433]], dtype=float32),'W2': array([[ 0.66262263, -0.41401526,  0.83104825, ..., -0.28790367,1.44923198, -0.01293663],[-0.94457793, -0.47847596,  0.39193049, ..., -0.44852871,0.31511024, -0.12879851],[ 0.83933985, -0.25525221,  1.83002853, ..., -0.7023285 ,0.29116887,  1.32396758],..., [-1.21769059,  0.21980943,  0.05707775, ..., -0.70724338,0.13368286, -0.47907224],[-0.78505909, -0.26749918, -1.0756464 , ...,  0.10546964,0.59970111, -0.47928923],[ 1.57277954,  0.20598291, -0.38545936, ..., -0.68153149,-0.01901394, -1.09839475]], dtype=float32),'W3': array([[ 0.23412205,  1.4664923 ,  1.02762878, ...,  0.13184339,1.05118167, -0.00358887],[ 0.26813394,  0.295957  ,  1.49240541, ...,  0.82661223,0.67465705, -0.32320595],[ 1.19123352, -0.83540916,  0.07576221, ..., -0.58284307,0.32790881,  0.13413283],..., [ 0.43964136,  1.74946868, -0.54555362, ..., -0.1613521 ,-0.37434128,  0.80795258],[ 0.60402709,  0.05262127,  0.42084417, ...,  0.47054997,-0.32987207, -1.64671504],[-0.78972542,  0.7970084 , -0.60551286, ...,  1.74413514,0.6057446 , -0.28617254]], dtype=float32),'b1': array([[-0.4571954 ],[-0.30936778],[-0.83330458],[-1.68725026],[-1.42897224],[-1.04096746],[-0.54966289],[ 2.43672371],[ 1.36083376],[-1.51412904],[-2.0457561 ],[-2.69589877],[-0.23028924],[ 0.88664472],[-1.48165977],[-2.08099437],[ 0.43034646],[ 0.7627002 ],[ 0.40478835],[-0.51313281],[-1.18395376],[-0.36716571],[-1.98513615],[-0.58582592],[-0.77087468],[-0.9414832 ],[ 0.25200051],[-0.98766547],[ 0.31909475],[ 0.0800764 ],[-0.01556224],[ 0.83097136],[ 0.32423681],[ 1.24688494],[-0.02111918],[-2.12303662],[-1.69796181],[ 0.68959635],[-0.6191389 ],[-1.28080022],[-0.17510706],[-0.23040138],[-0.46036553],[ 1.56836855],[ 2.0383904 ],[-0.86711407],[-1.19858789],[-1.96049547],[ 1.14845157],[-0.75677299],[-2.4980433 ],[ 0.13432245],[ 0.24774934],[-0.10357552],[ 0.93644065],[-1.22094846],[ 1.15299678],[ 1.51815248],[-0.20407377],[-0.76557356],[ 0.5967567 ],[ 1.13081288],[-0.34519741],[-0.18847673]], dtype=float32),'b2': array([[ 0.28188977],[ 1.13188219],[-0.51833898],[ 1.55272174],[ 0.3362346 ],[-0.62963486],[-0.55736727],[-1.99950421],[ 1.64439845],[ 0.09734726],[-2.69561672],[ 0.29041779],[ 0.72709852],[ 0.43301356],[-0.43779549],[-0.6581856 ],[-2.80175161],[-0.41372192],[-2.09087038],[-0.47786576],[ 0.31763604],[ 1.85912359],[ 1.59187448],[-1.36818421],[-0.65758836],[-0.12403597],[ 1.05362165],[-0.30393735],[ 1.8399303 ],[-0.29227388],[ 0.75677097],[ 0.3613534 ],[-0.18842472],[-0.66885817],[-0.27949655],[-0.89438319],[-1.51220632],[ 0.93994361],[-1.54467905],[-1.00363708],[-0.57895792],[-0.52491599],[ 2.27655602],[-0.85130656],[ 0.04630496],[ 1.12568331],[-0.38881832],[-0.27415273],[-0.86503613],[ 0.96864253],[-0.9870069 ],[ 0.37869945],[-1.68591571],[-0.62210619],[-0.01916602],[ 0.11517724],[-0.29602063],[-1.42557037],[ 1.11371112],[-1.10030782],[-0.23480549],[-0.83260995],[ 0.78863978],[-0.44784972],[ 0.18259326],[ 1.48195684],[-0.32906139],[-1.4134475 ],[ 0.52768463],[-0.46708786],[-1.52612662],[ 0.30641365],[-1.06699479],[-1.44061339],[-1.39849806],[-0.65535295],[-0.17019601],[ 0.86427599],[ 0.51089519],[ 0.63639545],[-0.31796476],[-0.96631444],[-1.21334612],[ 0.79893589],[ 0.90393507],[ 1.05157661],[-0.1798792 ],[ 0.35506439],[-0.88265395],[-0.77211195],[-0.35244057],[-0.97597492],[ 1.81438792],[ 1.50866187],[ 1.76945257],[-2.2490623 ],[ 1.27219939],[ 0.11137661],[-0.03369612],[ 1.64185321],[ 0.14421514],[ 1.1957972 ],[ 0.10298974],[-1.63592625],[ 1.57520294],[-2.0683074 ],[-0.78121209],[-0.02082653],[ 0.88429558],[ 0.98407972],[-1.09006429],[ 0.44493109],[-1.88774467],[-2.0510056 ],[-1.04833782],[ 1.08415902],[-1.55531442],[-1.52134264],[ 0.23356596],[-0.70101881],[-0.25792068],[ 0.41581729],[-0.11349884],[-3.29242682],[-0.68287402],[ 1.45735371],[ 0.07658232],[-0.82881683]], dtype=float32),'b3': array([[ 0.99828368],[-0.78877753],[-1.29528141],[-1.95668292],[ 1.43690228],[-0.19944769],[ 1.00068772],[ 0.8051874 ],[ 0.80680549],[ 0.26735926]], dtype=float32)}
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