def save(
bgfc0, wgfc0,
bgdc0, wgdc0,
bgdc1, wgdc1,
bgdc2, wgdc2,
bgdc3, wgdc3,
bdcv0, wdcv0,
bdcv1, wdcv1,
bdcv2, wdcv2,
bdcv3, wdcv3,
bdfc0, wdfc0
):
print("start save")
saveToFile = ConvNet.openEmptyFileW('gan9g.txt')
gfc0.save_ToFile(bgfc0, wgfc0, saveToFile)
gdc0.save_ToFile(bgdc0, wgdc0, saveToFile)
gdc1.save_ToFile(bgdc1, wgdc1, saveToFile)
gdc2.save_ToFile(bgdc2, wgdc2, saveToFile)
gdc3.save_ToFile(bgdc3, wgdc3, saveToFile)
saveToFile.flush();saveToFile.close()
saveToFile = ConvNet.openEmptyFileW('gan9d.txt')
dcv0.save_ToFile(bdcv0, wdcv0, saveToFile)
dcv1.save_ToFile(bdcv1, wdcv1, saveToFile)
dcv2.save_ToFile(bdcv2, wdcv2, saveToFile)
dcv3.save_ToFile(bdcv3, wdcv3, saveToFile)
dfc0.save_ToFile(bdfc0, wdfc0, saveToFile)
saveToFile.flush();saveToFile.close()
print("end save")
def save(idx, gSaver, dSaver):
print("start save")
saveToFile = ConvNet.openEmptyFileW("gan0g"+str(idx)+".txt")
for item in gSaver:
item(saveToFile)
saveToFile.flush();saveToFile.close()
saveToFile = ConvNet.openEmptyFileW("gan0d"+str(idx)+".txt")
for item in dSaver:
item(saveToFile)
saveToFile.flush();saveToFile.close()
print("end save")
def train():
with tf.Session(config=tf.ConfigProto(device_count = {'GPU': 0})) as sess:
#初始化参数
sess.run(tf.global_variables_initializer())
for j in range(0, 20):
#打印当前网络的输出值
a1 = sess.run(_net, feed_dict={inputlayer:verifydata1})
a2 = sess.run(_net, feed_dict={inputlayer:verifydata2})
a3 = sess.run(_net, feed_dict={inputlayer:verifydata3})
print(j, a1[0], a2[0], a3[0])#可以看到输出值越来越接近输入的值
#执行训练
for i in range(0, 100):
sess.run(optimizer, feed_dict={finaldata: indata, inputlayer: indata})
#保存已训练的网络
Saver = []
for item in plist:
Saver.append(item.getSaver(sess))
saveToFile = ConvNet.openEmptyFileW("test.txt")
for item in Saver:
item(saveToFile)
saveToFile.flush();saveToFile.close()
def train():
#with tf.Session(config=tf.ConfigProto(device_count = {'GPU': 0})) as sess:
with tf.Session() as sess:
#初始化参数
sess.run(tf.global_variables_initializer())
for j in xrange(0, 100):
#打印当前网络的输出值
accurate = 0
for _i in xrange(0,100):
testData,testLabel = MNISTData.extract_testdata()
lbl = sess.run(_test, feed_dict={testOne:testData})
if np.argmax(lbl, 1) == testLabel:
accurate = accurate + 0.01
#执行训练
totalLoss = 0.0
for _i in xrange(0,10):
trainData,trainLabel = MNISTData.extract_traindata(BATCH_SIZE)
_,_loss = sess.run([optimizer,loss], feed_dict={labels_node: trainLabel, inputlayer: trainData})
totalLoss = totalLoss + _loss
print(j,accurate,totalLoss)
#保存已训练的网络
Saver = []
for item in plist:
Saver.append(item.getSaver(sess))
saveToFile = ConvNet.openEmptyFileW("MNIST.txt")
for item in Saver:
item(saveToFile)
saveToFile.flush();saveToFile.close()
def train():
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saveimg("base0.bmp",CifarData,0)
saveimg("base1.bmp",CifarData,1)
saveimg("base2.bmp",CifarData,2)
saveimg("base3.bmp",CifarData,3)
saveimg("base4.bmp",CifarData,59110)
saveimg("base5.bmp",CifarData,59111)
saveimg("base6.bmp",CifarData,59112)
saveimg("base7.bmp",CifarData,59113)
verifydata[0] = CifarData[0]
verifydata[1] = CifarData[1]
verifydata[2] = CifarData[2]
verifydata[3] = CifarData[3]
verifydata[4] = CifarData[59110]
verifydata[5] = CifarData[59111]
verifydata[6] = CifarData[59112]
verifydata[7] = CifarData[59113]
for j in xrange(0, 1000):
for k in xrange(0,10):#train times
print(str(k)+' ',end='')
sys.stdout.flush()
for i in xrange(0, 800):#train range
for dj in xrange(0, BATCH_SIZE):
inputData[dj] = CifarData[dj+i*BATCH_SIZE]
sess.run(optimizer, feed_dict={finaldata: inputData, inputLayer: inputData})
print()
print(j)
_out = sess.run(regeneratedImg, feed_dict={inputLayer:verifydata})
saveimg(str(j)+"0.bmp",_out,0)
saveimg(str(j)+"1.bmp",_out,1)
saveimg(str(j)+"2.bmp",_out,2)
saveimg(str(j)+"3.bmp",_out,3)
saveimg(str(j)+"4.bmp",_out,4)
saveimg(str(j)+"5.bmp",_out,5)
saveimg(str(j)+"6.bmp",_out,6)
saveimg(str(j)+"7.bmp",_out,7)
print("saving")
testfile = ConvNet.openEmptyFileW("cifar"+str(j)+".txt")
conv1save(sess,testfile)
conv2save(sess,testfile)
conv3save(sess,testfile)
fc1saver(sess,testfile)
fc2saver(sess,testfile)
uconv1save(sess,testfile)
uconv2save(sess,testfile)
uconv3save(sess,testfile)
if testfile:testfile.flush(),testfile.close()
print("saved")
def train():
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels_node, logits=fc2))
batch = tf.Variable(0, dtype=tf.float32)
learning_rate = tf.train.exponential_decay(
0.01, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
train_size, # Decay step.
0.95, # Decay rate.
staircase=True)
# Use simple momentum for the optimization.
optimizer = tf.train.MomentumOptimizer(learning_rate,0.9).minimize(loss,global_step=batch)
# Create a local session to run the training.
start_time = time.time()
with tf.Session() as sess:
# Run all the initializers to prepare the trainable parameters.
tf.global_variables_initializer().run()
print('Initialized!')
# Loop through training steps.
for step in xrange(0,100):
# Compute the offset of the current minibatch in the data.
# Note that we could use better randomization across epochs.
offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)
batch_data = train_data[offset:(offset + BATCH_SIZE), ...]
batch_labels = train_labels[offset:(offset + BATCH_SIZE)]
# This dictionary maps the batch data (as a numpy array) to the
# node in the graph it should be fed to.
feed_dict = {inputLayer: batch_data,labels_node: batch_labels}
# Run the optimizer to update weights.
sess.run(optimizer, feed_dict=feed_dict)
# print some extra information once reach the evaluation frequency
if step % EVAL_FREQUENCY == 0:
# fetch some extra nodes' data
l, predictions = sess.run([loss, train_prediction],
feed_dict=feed_dict)
elapsed_time = time.time() - start_time
start_time = time.time()
print(elapsed_time,l, error_rate(predictions, batch_labels), error_rate(eval_in_batches(validation_data, sess), validation_labels))
print("save start")
testfile = ConvNet.openEmptyFileW('conv.txt')
conv1save(sess,testfile)
conv2save(sess,testfile)
fc1save(sess,testfile)
fc2save(sess,testfile)
if testfile:testfile.flush(),testfile.close()
print("save done")
def train():
with tf.Session() as sess:
#初始化参数
sess.run(tf.global_variables_initializer())
for j in range(0, 20):
#打印当前网络的输出值
a1 = sess.run(fc2, feed_dict={inputlayer:verifydata1})
a2 = sess.run(fc2, feed_dict={inputlayer:verifydata2})
a3 = sess.run(fc2, feed_dict={inputlayer:verifydata3})
print(j, a1[0], a2[0], a3[0])#可以看到输出值越来越接近输入的值
#执行训练
for i in range(0, 100):
sess.run(optimizer, feed_dict={finaldata: indata, inputlayer: indata})
#保存已训练的网络
testfile = ConvNet.openEmptyFileW('test.txt')
fc1saver(sess,testfile)
fc2saver(sess,testfile)
if testfile:testfile.flush(),testfile.close()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
ConvNet.outprint(sess.run(inputLayer, feed_dict={inputLayer: ind1}))
print()
ConvNet.outprint(sess.run(conv1, feed_dict={inputLayer: ind1}))
print()
ConvNet.outprint(sess.run(conv2, feed_dict={inputLayer: ind1}))
print()
ConvNet.outprint(sess.run(fc1, feed_dict={inputLayer: ind1}))
print()
ConvNet.outprint(sess.run(fc2, feed_dict={inputLayer: ind1}))
print()
ConvNet.outprint(sess.run(uconv1, feed_dict={inputLayer: ind1}))
print()
ConvNet.outprint(sess.run(uconv2, feed_dict={inputLayer: ind1}))
print()
testfile = ConvNet.openEmptyFileW('conv.txt')
conv1save(sess, testfile)
conv2save(sess, testfile)
fc1save(sess, testfile)
fc2save(sess, testfile)
uconv1save(sess, testfile)
uconv2save(sess, testfile)
if testfile:testfile.flush(), testfile.close()