convolutional:
import os
import model
import tensorflow as tf
import input_data
data=input_data.read_data_sets('MINST_data',one_hot=True)
with tf.variable_scope("convolutional"):
x=tf.placeholder(tf.float32,[None,784],name="x")
keep_prob=tf.placeholder(tf.float32)
y,variables=model.convolutional(x,keep_prob)
y_=tf.placeholder(tf.float32,[None,10],name='y')
cross_entropy=-tf.reduce_sum(y_*tf.log(y))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
saver=tf.train.Saver(variables)
with tf.Session() as sess:
merged_summary_op=tf.summary.merge_all()
summay_writer=tf.summary.FileWriter('/tmp/mnist_log/1',sess.graph)
summay_writer.add_graph(sess.graph)
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch=data.train.next_batch(50)
if i%100==0:
train_accuracy=accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
print("step %d, training accuracy %g"%(i,train_accuracy))
sess.run(train_step,feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})
print(sess.run(accuracy,feed_dict={x:data.test.images,y_:data.test.labels,keep_prob:1.0}))
path=saver.save(
sess,os.path.join(os.path.dirname(__file__),'data','convolutional.ckpt'),
write_meta_graph=False,write_state=False)
print("Saved:",path)
model:
import tensorflow as tf
def regression(x):
W=tf.Variable(tf.zeros([784,10]),name="W")
b=tf.Variable(tf.zeros([10]),name="b")
y=tf.nn.softmax(tf.matmul(x,W)+b)
return y,[W,b]
def convolutional(x,keep_prob):
def conv2d(x,W):
return tf.nn.conv2d([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])
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)
x_image=tf.reshape(x,[-1,28,28,1])
W_conv1=weight_variable([5,5,1,32])
b_conv1=bias_variable([32])
h_convl=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
h_pool1=max_pool_2x2(h_convl)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1=weight_variable([7*7*64,1024])
b_fc1=bias_variable([1024])
h_pool2_flat=tf.reshape(h_pool2,[-1,7*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)
W_fc2=weight_variable([1024,10])
b_fc2=bias_variable([10])
y=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
return y,[W_conv1,b_conv1,W_conv2,b_conv2,W_fc1,b_fc1,W_fc2,b_fc2]
报错如下：
WARNING:tensorflow:From D:\Python\workspace\Pybasis\minist\model.py:17: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead.Traceback (most recent call last): File "D:/Python/workspace/Pybasis/minist/convolutional.py", line 11, in <module> y,variables=model.convolutional(x,keep_prob) File "D:\Python\workspace\Pybasis\minist\model.py", line 26, in convolutional h_convl=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1) File "D:\Python\workspace\Pybasis\minist\model.py", line 12, in conv2d return tf.nn.conv2d([1,1,1,1],padding="SAME") File "D:\Python\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1953, in conv2d name=name) File "D:\Python\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 1070, in conv2d data_format=data_format, dilations=dilations, name=name) File "D:\Python\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 626, in _apply_op_helper param_name=input_name) File "D:\Python\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 60, in _SatisfiesTypeConstraint ", ".join(dtypes.as_dtype(x).name for x in allowed_list)))TypeError: Value passed to parameter 'input' has DataType int32 not in list of allowed values: float16, bfloat16, float32, float64Process finished with exit code 1
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