跑了一个完整tensorflow示例，通过定义简单的一个layer，搭建网络，定义训练参数，进行训练后即可进行较为精确的预测。很神奇的是，简单几行代码，即可得到一个相当精确的预测模型。
layer定义： 就是y = wx + b 简单的仿射变化，输入参数包括输入维数，输出维数，输入数据，激活函数
网络定义：
- 输入层简单定义为一个placeholder，输入个数待定，维数为784个像素的图片数据
- 隐藏层：layer，输入参数个数为784，输出参数个数256，输入数据为输入层x，激活函数ReLU
- 输出层：输出10个数字，输入为隐藏层输出，无激活函数
训练参数：
- 损失函数：reduce mean标准的
- 优化器： adam优化器，参数为学习率，目标为最小化损失函数
- 精度：准确预测的平均值
训练：
- 迭代次数
- 每次处理的数据
- 运行优化器
- 获取损失和精度（不是必须的）
- 精度评估
- 预测
精度提高：
- 增加隐藏层，增加隐藏层的网络参数256->1000
- 精度从0.94提高到0.96
数据可以直接从 http://yann.lecun.com/exdb/mnist/ 下载，放到相应的目录即可。
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import matplotlib.pyplot as plt
import numpy as np
from time import time
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
mnist = input_data.read_data_sets("data/MNIST_data/", one_hot = True)
print(" train: ", mnist.train.num_examples)
print("validation: ", mnist.validation.num_examples)
print(" test: ", mnist.test.num_examples)
print("image shape: ", mnist.train.images.shape)
print("label shape: ", mnist.train.labels.shape)
def show_image(image):
plt.imshow(image.reshape(28, 28), cmap = 'binary')
plt.show()
def plot_image_label_prediction(images, labels, prediction = [], idx = 0, num = 10):
fig = plt.gcf()
fig.set_size_inches(12, 14)
if num > 25:
num = 25
for i in range(0, num):
ax = plt.subplot(5, 5, 1 + i)
ax.imshow(np.reshape(images[idx], (28, 28)), cmap="binary")
title = "label = " + str(np.argmax(labels[idx]))
if len(prediction) > 0:
title += ", prediction = " + str(prediction[idx])
ax.set_title(title, fontsize = 10)
ax.set_xticks([])
ax.set_yticks([])
idx += 1
plt.show()
#show_image(mnist.train.images[0])
print("labels]0]: ", mnist.train.labels[0])
print("labels[0]: ", np.argmax(mnist.train.labels[0]))
#plot_image_label_prediction(mnist.train.images, mnist.train.labels)
#batch_images, batch_labels = mnist.train.next_batch(batch_size = 100)
#plot_image_label_prediction(batch_images, batch_labels)
def layer(output_dim, input_dim, inputs, activation = None):
W = tf.Variable(tf.random_normal([input_dim, output_dim]))
b = tf.Variable(tf.random_normal([1, output_dim]))
XWb = tf.matmul(inputs, W) + b
if activation is None:
outputs = XWb
else:
outputs = activation(XWb)
return outputs
x = tf.placeholder("float", [None, 784])
h1 = layer(output_dim = 256, input_dim = 784, inputs = x, activation = tf.nn.relu)
y_predict = layer(output_dim = 10, input_dim = 256, inputs = h1, activation = None)
y_label = tf.placeholder("float", [None, 10])
loss_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = y_predict, labels = y_label))
optimizer = tf.train.AdamOptimizer(learning_rate = 0.001).minimize(loss_function)
correct_predict = tf.equal(tf.argmax(y_label, 1), tf.argmax(y_predict, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predict, "float"))
train_epochs = 15
batch_size = 100
total_batches = int(mnist.train.num_examples/batch_size)
loss_list = []
epoch_list = []
accuracy_list = []
start_time = time()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for epoch in range(train_epochs):
for i in range(total_batches):
batch_x, batch_y = mnist.train.next_batch(batch_size)
sess.run(optimizer, feed_dict = {x: batch_x, y_label: batch_y})
loss, acc = sess.run([loss_function, accuracy],
feed_dict = {x: mnist.validation.images, y_label: mnist.validation.labels})
epoch_list.append(epoch)
loss_list.append(loss)
accuracy_list.append(acc)
print("Train Epoch: ", "%2d, " % (epoch + 1),
"Loss = {:.9f}, ".format(loss),
"Accuracy = ", acc)
duration = time() - start_time
print("Train finished takes: ", duration)
print("Accuracy: ", sess.run(accuracy, feed_dict={x:mnist.test.images, y_label:mnist.test.labels}))
prediction_result = sess.run(tf.argmax(y_predict, 1), feed_dict={x: mnist.test.images})
print("predict result: ", prediction_result[:10])
plot_image_label_prediction(mnist.test.images, mnist.test.labels, prediction_result, num = 25)
sess.close()
训练结果
Train Epoch: 1, Loss = 6.796162605, Accuracy = 0.8338
Train Epoch: 2, Loss = 4.300796986, Accuracy = 0.8828
Train Epoch: 3, Loss = 3.252708912, Accuracy = 0.9038
Train Epoch: 4, Loss = 2.677492619, Accuracy = 0.915
Train Epoch: 5, Loss = 2.369313955, Accuracy = 0.9196
Train Epoch: 6, Loss = 2.162565947, Accuracy = 0.9248
Train Epoch: 7, Loss = 1.811923862, Accuracy = 0.9334
Train Epoch: 8, Loss = 1.715990782, Accuracy = 0.933
Train Epoch: 9, Loss = 1.545861244, Accuracy = 0.9396
Train Epoch: 10, Loss = 1.475827694, Accuracy = 0.9406
Train Epoch: 11, Loss = 1.449908972, Accuracy = 0.9404
Train Epoch: 12, Loss = 1.376323223, Accuracy = 0.9424
Train Epoch: 13, Loss = 1.375033021, Accuracy = 0.9402
Train Epoch: 14, Loss = 1.258133173, Accuracy = 0.9446
Train Epoch: 15, Loss = 1.269988537, Accuracy = 0.9422
Train finished takes: 23.701005935668945
Accuracy: 0.9428
识别结果
