我正在训练CNN以在Tensorflow中进行回归。但是，我的CNN不能正常运行。当我将训练后的数据提供给CNN时，它将得到一个大小为100 * 100且值为零的矩阵。我的输入是大小为200 * 100的图像，而我的目标或CNN的输出是大小为100 * 100的图像。如果有人在这方面能帮助我，我将非常感激。这是我的代码：
import tensorflow as tf
import numpy as np
#from Dataset_Preprocessing import Create_Train_Validation_Test
batch_size = 64
imag_size = 100;
num_channels = 1;
Learning_rate1 = 1e-3
TRAIN_DIR = 'Image/'
TEST_DIR = 'Label/'
MODEL_NAME = 'Image_Reconstruction_CNN-{}-{}-model'.format('5_convolutional_layer', 'HOJAT')
# Loading Data
Train_X = np.load('Train_X.npy')
Train_Y = np.load('Train_Y.npy')
Validation_X = np.load('Validation_X.npy')
Validation_Y = np.load('Validation_Y.npy')
Test_X = np.load('Test_X.npy')
Test_Y = np.load('Test_Y.npy')
x = tf.placeholder(tf.float32, shape=[None, 2*imag_size,imag_size,1], name='x')
y = tf.placeholder(tf.float32, shape=[None, imag_size,imag_size,1], name='y')
"""
defined functions
>>>
>>>
>>>
"""
def create_weights(shape):
return tf.Variable(tf.truncated_normal(shape, stddev=0.05))
def create_biases(shape):
return tf.Variable(tf.truncated_normal([shape], stddev=0.05))
def create_convolutional_layer_ReLU(data, num_input_channels, conv_filter_size, num_filters):
weights = create_weights(shape = [conv_filter_size, conv_filter_size, num_input_channels, num_filters])
print(weights)
biases = create_biases(shape = num_filters)
layer = tf.nn.conv2d(data, weights, strides = [1, 1, 1, 1], padding = 'SAME')
layer += biases
layer = tf.nn.relu(layer)
return layer
def create_convolutional_layer_BN_ReLU(data, num_input_channels, conv_filter_size, num_filters):
weights = create_weights(shape = [conv_filter_size, conv_filter_size, num_input_channels, num_filters])
print(weights)
biases = create_biases(shape = num_filters)
layer = tf.nn.conv2d(data, weights, strides = [1, 1, 1, 1], padding = 'SAME')
layer += biases
layer = tf.layers.batch_normalization(layer)
layer = tf.nn.relu(layer)
return layer
"""
>>>
>>>
>>>
end of defined functions
>>>
>>>
>>>
"""
def Image_Reconstruction_CNN(x):
layer_1 = create_convolutional_layer_ReLU(x,num_input_channels = 1, conv_filter_size = 3, num_filters= 64)
layer_2 = create_convolutional_layer_BN_ReLU(layer_1,num_input_channels = 64, conv_filter_size = 3, num_filters= 64)
layer_2 = tf.nn.dropout(layer_2,0.7)
layer_3 = create_convolutional_layer_BN_ReLU(layer_2,num_input_channels = 64, conv_filter_size = 3, num_filters= 64)
layer_3 = tf.nn.dropout(layer_3,0.7)
layer_3 = tf.nn.max_pool(layer_3, ksize = [1,100,1,1], strides = [1,2,1,1], padding = 'SAME')
layer_4 = create_convolutional_layer_BN_ReLU(layer_3,num_input_channels = 64, conv_filter_size = 3, num_filters= 64)
layer_5 = create_convolutional_layer_BN_ReLU(layer_4,num_input_channels = 64, conv_filter_size = 3, num_filters= 1)
return layer_5
#Helper Function For Showing Progress of The Network
#def show_progress(epoch, feed_dict_train, feed_dict_validate, val_loss):
# acc = session.run(accuracy, feed_dict=feed_dict_train)
# val_acc = session.run(accuracy, feed_dict=feed_dict_validate)
# msg = "Training Epoch {0} --- Training Accuracy: {1:>6.1%}, Validation Accuracy: {2:>6.1%}, Validation Loss: {3:.3f}"
# print(msg.format(epoch + 1, acc, val_acc, val_loss))
Save_Dir = 'MODEL_SAVER_2/'
from scipy.io import savemat
prediction = Image_Reconstruction_CNN(x)
Loss = tf.losses.mean_squared_error(y,prediction)
cost = tf.reduce_mean(Loss)
optimizer = tf.train.AdamOptimizer(learning_rate= Learning_rate1).minimize(cost)
accuracy = tf.metrics.mean_absolute_error(y,prediction)
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
hm_epochs = 1
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
sess.run(init_g)
sess.run(init_l)
saver = tf.train.Saver()
for epoch in range(hm_epochs):
epoch_loss = 0
k=0
batch_id=1
while k< 2605:
start=k
end=k+batch_size
batch_x = np.array(Train_X[start:end])
batch_y = np.array(Train_Y[start:end])
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
epoch_loss += c
k += batch_size
print('Epoch {}. Batch {} out of 41. Epoch {}, Batch {} loss is {}'.format(
epoch+1,batch_id,epoch+1,batch_id,epoch_loss))
with open("RESULT.txt","a") as text:
print('Epoch {}. Batch {} out of {}. Epoch {}, Batch {} loss is {}'.format(
epoch+1, int(Train_X.shape[0]/batch_size)+1, batch_id, epoch+1, batch_id,epoch_loss), file = text)
batch_id += 1
saver.save(sess,Save_Dir)
result1 = sess.run(prediction, feed_dict={x: Train_X[0:50], y: Train_Y[0:50]})
savemat('result1.mat',mdict={'result1':result1})
print('')
print('Epoch {} completed out of {} Epochs. Epoch {}, TOTAL Loss is {}'.format(epoch+1,hm_epochs,epoch+1,epoch_loss))
with open("RESULT.txt","a") as text:
print('',file= text)
print('Epoch {} completed out of {} Epochs. Epoch {}, TOTAL Loss is {}'.format(
epoch+1,hm_epochs,epoch+1,epoch_loss),file= text)
acc = sess.run(accuracy, feed_dict={x: Validation_X, y: Validation_Y})
print('Accuracy:', acc)
print('')
with open("RESULT.txt","a") as text:
print('Accuracy:', acc, file=text)
print('',file=text)