利用tensorflow 实现多分类问题，数据为mnist
import tensorflow as tf
import numpy as np
from sklearn import datasets
from tensorflow.examples.tutorials.mnist import input_data
def read_file():
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
train_x, train_y, test_x, test_y = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
print ("downloading")
return train_x, train_y, test_x, test_y
def weights_biases_placeholder(n_dim, n_classes):
X = tf.placeholder(tf.float32, [None, n_dim])
Y = tf.placeholder(tf.float32, [None, n_classes])
w = tf.Variable(tf.random_normal([n_dim, n_classes], stddev=0.01), name='weights')
b = tf.Variable(tf.random_normal([n_classes]), name='biases')
return X, Y, w, b
def forward_pass(X, w, b):
out = tf.matmul(X, w) + b
return out
def multiclass_cost(out, Y):
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out, labels=Y))
return cost
def init():
return tf.global_variables_initializer()
def train_op(learning_rate, cost):
op_train = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
return op_train
train_x, train_y, test_x, test_y = read_file()
X, Y, w, b = weights_biases_placeholder(train_x.shape[1], train_y.shape[1])
out = forward_pass(X, w, b)
cost = multiclass_cost(out, Y)
learning_rate, epochs = 0.01, 1000
op_train = train_op(learning_rate, cost)
init = init()
loss_trace = []
accuracy_trace = []
with tf.Session() as sess:
sess.run(init)
for i in range(epochs):
sess.run(op_train, feed_dict={X: train_x, Y: train_y})
loss_ = sess.run(cost, feed_dict={X: train_x, Y: train_y})
accuracy_ = np.mean(np.argmax(sess.run(out, feed_dict={X: train_x, Y: train_y}), axis=1) == np.argmax(train_y, axis=1))
loss_trace.append(loss_)
accuracy_trace.append(accuracy_)
if (((i+1)>100) and ((i+1)%100==0)):
print ("Epoch: ",(i+1)," loss: ", loss_, " accuracy:", accuracy_)
print ("Final training result----- ", "loss:", loss_, " accuracy: ", accuracy_)
loss_test = sess.run(cost,feed_dict={X: test_x, Y: test_y})
test_pred = np.argmax(sess.run(out, feed_dict={X: test_x, Y: test_y}), axis=1)
accuracy_test = np.mean(test_pred == np.argmax(test_y, axis=1))
print ("Result on test data-----", "loss:", loss_test, " accuracy:", accuracy_test)
