我现在正在学习一周的张量流，并尝试从斯坦福的代码（下面的代码）开始为CNN编写课程
import os
import tensorflow as tf
from time import time
class Cnn:
def __init__(self, batch_size=128, skip_step=10, epochs=1, dropout_ratio=0.75):
self.batch_size = batch_size
self.skip_step = skip_step
self.epochs = epochs
self.dropout_ratio = dropout_ratio
self.x = tf.placeholder(tf.float32, [None, 784], name="X_placeholder")
self.images = tf.reshape(self.x, shape=[-1, 28, 28, 1])
self.y = tf.placeholder(tf.float32, [None, 10], name="Y_placeholder")
self.predictions = tf.placeholder(tf.float32, [None, 10], name="predictions")
self.dropout = tf.placeholder(tf.float32, name="dropout")
self.global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')
self.layers = [self.images]
self.n_layers = 0
def convolution(self, kernel_size, n_output, strides, scope_name):
previous_layer = self.layers[self.n_layers]
with tf.variable_scope(scope_name) as scope:
kernel_shape = [kernel_size[0], kernel_size[1], previous_layer.get_shape()[3], n_output]
kernel = tf.get_variable("kernels", kernel_shape,
initializer=tf.truncated_normal_initializer())
biases = tf.get_variable("biases", [n_output],
initializer=tf.random_normal_initializer())
convolution_2d = tf.nn.conv2d(previous_layer, kernel, strides=strides,
padding="SAME")
convolution = tf.nn.relu(convolution_2d + biases, name=scope.name)
self.layers.append(convolution)
self.n_layers += 1
return convolution
def pool(self, size, strides, scope_name, padding="SAME"):
previous_layer = self.layers[self.n_layers]
with tf.variable_scope(scope_name):
pool = tf.nn.max_pool(previous_layer, ksize=size, strides=strides,
padding=padding)
self.layers.append(pool)
self.n_layers += 1
return pool
def fully_connected(self, n_input, n_output, scope_name, relu=False, dropout=True):
previous_layer = self.layers[self.n_layers]
with tf.variable_scope(scope_name):
weights = tf.get_variable("weights", [n_input, n_output],
initializer=tf.truncated_normal_initializer())
biases = tf.get_variable("biases", [n_output],
initializer=tf.random_normal_initializer())
pool_reshaped = tf.reshape(previous_layer, [-1, n_input])
logits = tf.add(tf.matmul(pool_reshaped, weights), biases)
fc = logits
if relu:
fc = tf.nn.relu(fc, name="relu")
if dropout:
fc = tf.nn.dropout(fc, self.dropout, name="dropout")
self.layers.append(fc)
self.n_layers += 1
if relu is False and dropout is False:
self.logits = tf.add(tf.matmul(pool_reshaped, weights), biases, name="logits")
self.predictions = tf.nn.softmax(logits, name="predictions")
return fc
def set_loss(self, scope_name):
previous_layer = self.layers[self.n_layers]
with tf.name_scope(scope_name):
entropy = tf.nn.softmax_cross_entropy_with_logits(logits=previous_layer, labels=self.y)
loss = tf.reduce_mean(entropy, name='loss')
self.layers.append(loss)
self.n_layers += 1
self.loss = loss
def set_optimizer(self):
previous_layer = self.layers[self.n_layers]
optimizer = tf.train.AdamOptimizer(0.001).minimize(previous_layer, global_step=self.global_step)
self.layers.append(optimizer)
self.n_layers += 1
self.optimizer = optimizer
def run(self, train_x, train_y):
sess = tf.Session()
with sess.as_default():
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
writer = tf.summary.FileWriter('./my_graph/mnist', sess.graph)
ckpt = tf.train.get_checkpoint_state(os.path.dirname('results/checkpoint'))
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
initial_step = self.global_step.eval()
start_time = time()
n_batches = int(train_x.shape[0] / self.batch_size)
total_correct_preds = 0
total_loss = 0.0
current_position = 0
for index in range(initial_step, int(n_batches * self.epochs)): # train the model n_epochs times
x_batch = train_x[current_position: current_position + self.batch_size, :]
y_batch = train_y[current_position: current_position + self.batch_size, :]
feed_dict = {self.x: x_batch, self.y: y_batch, self.dropout: self.dropout_ratio}
_, loss_batch, logits_batch = sess.run([self.optimizer, self.loss, self.logits],
feed_dict=feed_dict)
total_loss += loss_batch
preds = tf.nn.softmax(logits_batch)
correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(y_batch, 1))
accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32))
total_correct_preds += sess.run(accuracy)
if (index + 1) % self.skip_step == 0:
print('Average loss at step {}: {:5.1f}'.format(index + 1, total_loss / self.skip_step))
total_loss = 0.0
saver.save(sess, 'results/mnist-convnet', index)
current_position += self.batch_size
print("Optimization Finished!") # should be around 0.35 after 25 epochs
print("Total time: {0} seconds".format(time() - start_time))
print("Accuracy {0}".format(total_correct_preds / train_x.shape[0]))
def test(self, val_x, val_y):
print("start testing")
checkpoint_file = tf.train.latest_checkpoint("results/")
graph = tf.Graph()
with graph.as_default():
sess = tf.Session()
with sess.as_default():
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)
self.x = graph.get_operation_by_name("X_placeholder").outputs[0]
self.y = graph.get_operation_by_name("Y_placeholder").outputs[0]
self.dropout = graph.get_operation_by_name("dropout").outputs[0]
self.global_step = graph.get_operation_by_name("global_step").outputs[0]
self.logits = graph.get_operation_by_name("logits").outputs[0]
current_position = 0
n_batches = int(val_x.shape[0] / self.batch_size)
total_correct_preds = 0
for i in range(n_batches):
x_batch = val_x[current_position: current_position + self.batch_size]
y_batch = val_y[current_position: current_position + self.batch_size]
feed_dict = {self.x: x_batch, self.y: y_batch, self.dropout: self.dropout_ratio}
logits_batch = sess.run(self.logits, feed_dict=feed_dict)
preds = tf.nn.softmax(logits_batch)
correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(y_batch, 1))
accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32))
total_correct_preds += sess.run(accuracy)
current_position += self.batch_size
print("Accuracy {0}".format(total_correct_preds / val_x.shape[0]))
测试代码
from cnn import Cnn
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
tf.set_random_seed(1)
N_CLASSES = 10
# load datas
mnist = input_data.read_data_sets("mnist", one_hot=True)
train_x, train_y = mnist.train.images, mnist.train.labels
val_x, val_y = mnist.test.images, mnist.test.labels
model = Cnn(batch_size=128, skip_step=10, epochs=0.25, dropout_ratio=0.75)
model.convolution(kernel_size=[5, 5], strides=[1, 1, 1, 1], n_output=32, scope_name="conv1")
model.pool(size=[1, 2, 2, 1], strides=[1, 2, 2, 1], scope_name="pool1")
model.convolution(kernel_size=[5, 5], strides=[1, 1, 1, 1], n_output=64, scope_name="conv2")
model.pool(size=[1, 2, 2, 1], strides=[1, 2, 2, 1], scope_name="pool2")
input_features = 7 * 7 * 64
model.fully_connected(n_input=input_features, n_output=1024, scope_name="fc", relu=True, dropout=True)
model.fully_connected(n_input=1024, n_output=N_CLASSES, scope_name="softmax_linear", dropout=False)
model.set_loss(scope_name="loss")
model.set_optimizer()
model.run(train_x=train_x, train_y=train_y)
model.test(val_x, val_y)
评论这一行 self.predictions = tf.placeholder（tf.float32，[None，10]，name =＆＃34; predictions＆＃34;）更改培训结果。
有人已经遇到过这个问题吗？
谢谢！