import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data',one_hot=True)
def compute_accuracy(v_xs,v_ys):
global prediction
y_pre = sess.run(prediction,feed_dict={xs:v_xs})
correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys})
return result
def weight_variable(shape):
initial = tf.truncated_normal(shape,stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1,shape = shape)
return tf.Variable(initial)
def conv2d(x,W):
#strides=[1,x_movement,y_movement,1]
#Must have strides[0]=strides[3]=1
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
#define placeholder for inputs to network
xs = tf.placeholder(tf.float32,[None,784]) #28*28
ys = tf.placeholder(tf.float32,[None,10])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs,[-1,28,28,1])
#print(x_image.shape) #[n_samples,28,28,1]
##conv1 layer##
W_conv1 = weight_variable([5,5,1,32])#patch 5*5,in size 1,out size 32
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)#output size 28*28*32
h_pool1 = max_pool_2x2(h_conv1)#output size 14*14*32
##conv2 layer##
W_conv2 = weight_variable([5,5,32,64])#patch 5*5,in size 32,out size 64
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)#output size 14*14*64
h_pool2 = max_pool_2x2(h_conv2)#output size 7*7*64
##func1 layer##
W_fc1 = weight_variable([7*7*64,1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])#[n_samples,7,7,64] ->> [n_samples,7*7*64]
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)
##func2 layer##
W_fc2 = weight_variable([1024,10])
b_fc2 = bias_variable([10])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
#the error between prediction and the real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1])) #loss
train_step = tf.train.AdamOptimizer(1e-4).minimize((cross_entropy))
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
if i % 50 == 0:
print(compute_accuracy(mnist.test.images,mnist.test.labels))
上述是代码 也是我学来的 但是却在报错Traceback (most recent call last):
File "D:/aibuild/CNN.py", line 68, in
sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
File "D:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 900, in run
run_metadata_ptr)
File "D:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1135, in _run
feed_dict_tensor, options, run_metadata)
File "D:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1316, in _do_run
run_metadata)
File "D:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1335, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_2' with dtype float
[[Node: Placeholder_2 = Placeholderdtype=DT_FLOAT, shape=, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
Caused by op 'Placeholder_2', defined at:
File "D:/aibuild/CNN.py", line 32, in
keep_prob = tf.placeholder(tf.float32)
File "D:\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1808, in placeholder
return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
File "D:\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 5835, in placeholder
"Placeholder", dtype=dtype, shape=shape, name=name)
File "D:\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in apply_op_helper
op_def=op_def)
File "D:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3392, in create_op
op_def=op_def)
File "D:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1718, in __init_
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_2' with dtype float
[[Node: Placeholder_2 = Placeholderdtype=DT_FLOAT, shape=, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
Process finished with exit code 1
求大佬解决
