[置顶] 【tensorflow 全连接神经网络】 minist 手写数字识别

# 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 math

mnist = 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.labels

def 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, integer

Returns:
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 partitionning
for 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 network
for 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) + b1
A1 = tf.nn.relu(Z1)  # A1 = relu(Z1)
Z2 = tf.add(tf.matmul(W2, A1), b2)  # Z2 = np.dot(W2, a1) + b2
A2 = tf.nn.relu(Z2)  # A2 = relu(Z2)
Z3 = tf.add(tf.matmul(W3, A2), b3)  # Z3 = np.dot(W3,Z2) + b3

return Z3

def compute_cost(Z3, Y):
"""
Computes the cost

Arguments:
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 Z3

Returns:
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 cost

def 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 size
costs = [] # to keep track of the cost
X = 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 epoch
num_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_minibatches
if 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 cost
plt.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 parameters

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)

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: 0.023649

Parameters have been trained!
Train Accuracy: 0.994545
Test Accuracy: 0.9427
Out[106]:
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-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],
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[ 0.88664472],
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[ 0.43034646],
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'b2': array([[ 0.28188977],
[ 1.13188219],
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[ 1.55272174],
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[ 1.08415902],
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[ 0.23356596],
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[ 0.41581729],
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[ 1.45735371],
[ 0.07658232],
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'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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