我在我的Pycharm中编写了以下代码，该代码在Tensorflow中执行完全连接层（FCL）。占位符发生无效的参数错误。因此，我在占位符中输入了所有dtype，shape和name，但仍然收到无效的参数错误。
我想通过FCL模型制作新的Signal（1，222）。
输入信号（1，222）=>输出信号（1，222）
maxPredict：在输出信号中找到具有最高值的索引。 calculate Y：获取与maxPredict相对应的频率数组值。loss：使用真实Y之间的差并将Y计算为损失。loss = tf.abs（trueY -calculateY）` 代码（发生错误）
x = tf.placeholder(dtype=tf.float32, shape=[1, 222], name='inputX')
错误
InvalidArgumentError（请参见上面的回溯）：您必须使用dtype float和shape [1,222]输入占位符张量“ inputX”的值 tensorflow.python.framework.errors_impl.InvalidArgumentError：您必须使用dtype float和shape [1,222]输入占位符张量“ inputX”的值 [[{{node inputX}} = Placeholderdtype = DT_FLOAT，shape = [1,222]，_ device =“ / job：localhost /副本：0 / task：0 / device：CPU：0”]] 在处理上述异常期间，发生了另一个异常：
我更改了密码。
x = tf.placeholder(tf.float32, [None, 222], name='inputX')
错误案例1
tensorFreq = tf.convert_to_tensor(basicFreq, tf.float32)
newY = tf.gather(tensorFreq, maxPredict) * 60
loss = tf.abs(y - tf.Variable(newY))
ValueError：initial_value必须具有指定的形状：Tensor（“ mul：0”，shape =（?,），dtype = float32）
错误案例2
tensorFreq = tf.convert_to_tensor(basicFreq, tf.float32)
newY = tf.gather(tensorFreq, maxPredict) * 60
loss = tf.abs(y - newY)
回溯（最近通话最近）： 文件“ D：/PycharmProject/DetectionSignal/TEST_FCL_StackOverflow.py”，第127行，在 trainStep = opt.minimize（损失） 最小的文件“ C：\ Users \ Heewony \ Anaconda3 \ envs \ TSFW_pycharm \ lib \ site-packages \ tensorflow \ python \ training \ optimizer.py”，第407行 （[_的str（v），grads_and_vars中的v，损失）） ValueError：没有为任何变量提供渐变，请在变量[tf.Variable'Variable：0'shape =（222，1024）dtype = float32_ref，tf之间检查是否有不支持渐变的操作 .variable'Variable_1：0'shape =（1024，）dtype = float32_re，......... tf.variable'Variable_5：0'shape =（222，）dtype = float32_ref]和损失张量（“ Abs：0”，dtype = float32）。
def Model_FCL(inputX):
data = inputX # input Signals
# Fully Connected Layer 1
flatConvh1 = tf.reshape(data, [-1, 222])
fcW1 = tf.Variable(tf.truncated_normal(shape=[222, 1024], stddev=0.05))
fcb1 = tf.Variable(tf.constant(0.1, shape=[1024]))
fch1 = tf.nn.relu(tf.matmul(flatConvh1, fcW1) + fcb1)
# Fully Connected Layer 2
flatConvh2 = tf.reshape(fch1, [-1, 1024])
fcW2 = tf.Variable(tf.truncated_normal(shape=[1024, 1024], stddev=0.05))
fcb2 = tf.Variable(tf.constant(0.1, shape=[1024]))
fch2 = tf.nn.relu(tf.matmul(flatConvh2, fcW2) + fcb2)
# Output Layer
fcW3 = tf.Variable(tf.truncated_normal(shape=[1024, 222], stddev=0.05))
fcb3 = tf.Variable(tf.constant(0.1, shape=[222]))
logits = tf.add(tf.matmul(fch2, fcW3), fcb3)
predictY = tf.nn.softmax(logits)
return predictY, logits
def loadMatlabData(fileName):
contentsMat = sio.loadmat(fileName)
dataInput = contentsMat['dataInput']
dataLabel = contentsMat['dataLabel']
dataSize = dataInput.shape
dataSize = dataSize[0]
return dataInput, dataLabel, dataSize
def getNextSignal(num, data, labels, WINDOW_SIZE, OUTPUT_SIZE):
shuffleSignal = data[num]
shuffleLabels = labels[num]
# shuffleSignal = shuffleSignal.reshape(1, WINDOW_SIZE)
# shuffleSignal = np.asarray(shuffleSignal, np.float32)
return shuffleSignal, shuffleLabels
def getBasicFrequency():
# basicFreq => shape(222)
basicFreq = np.array([0.598436736688, 0.610649731314, ... 3.297508549096])
return basicFreq
basicFreq = getBasicFrequency()
myGraph = tf.Graph()
with myGraph.as_default():
# define input data & output data 입력받기 위한 placeholder
x = tf.placeholder(dtype=tf.float32, shape=[1, 222], name='inputX') # Signal size = [1, 222]
y = tf.placeholder(tf.float32, name='trueY') # Float value size = [1]
print('inputzz ', x, y)
print('Graph ', myGraph.get_operations())
print('TrainVariable ', tf.trainable_variables())
predictY, logits = Model_FCL(x) # Predict Signal, size = [1, 222]
maxPredict = tf.argmax(predictY, 1, name='maxPredict') # Find max index of Predict Signal
tensorFreq = tf.convert_to_tensor(basicFreq, tf.float32)
newY = tf.gather(tensorFreq, maxPredict) * 60 # Find the value that corresponds to the Freq array index
loss = tf.abs(y - tf.Variable(newY)) # Calculate absolute (true Y - predict Y)
opt = tf.train.AdamOptimizer(learning_rate=0.0001)
trainStep = opt.minimize(loss)
print('Graph ', myGraph.get_operations())
print('TrainVariable ', tf.trainable_variables())
with tf.Session(graph=myGraph) as sess:
sess.run(tf.global_variables_initializer())
dataFolder = './'
writer = tf.summary.FileWriter('./logMyGraph', sess.graph)
startTime = datetime.datetime.now()
numberSummary = 0
accuracyTotalTrain = []
for trainEpoch in range(1, 25 + 1):
arrayTrain = []
dataPPG, dataLabel, dataSize = loadMatlabData(dataFolder + "TestValues.mat")
for i in range(dataSize):
batchSignal, valueTrue = getNextSignal(i, dataPPG, dataLabel, 222, 222)
_, lossPrint, valuePredict = sess.run([trainStep, loss, newY], feed_dict={x: batchSignal, y: valueTrue})
print('Train ', i, ' ', valueTrue, ' - ', valuePredict, ' Loss ', lossPrint)
arrayTrain.append(lossPrint)
writer.add_summary(tf.Summary(value=[tf.Summary.Value(tag='Loss', simple_value=float(lossPrint))]),
numberSummary)
numberSummary += 1
accuracyTotalTrain.append(np.mean(arrayTrain))
print('Final Train : ', accuracyTotalTrain)
sess.close()
答案 0 :(得分：0)
似乎变量batchSignal的类型或形状错误。它必须是一个形状完全为[1, 222]的numpy数组。如果要使用批量 n ×222的示例，占位符x的形状应为[None, 222]，占位符y的形状应为{{1 }}。
顺便说一句，考虑使用tf.layers.dense而不是显式初始化变量并自己实现层。
答案 1 :(得分：0)
应该改变两件事。
错误案例0。您无需重新调整图层之间的流程。您可以在第一个维度上使用None来传递动态批量大小。
错误情况1。您可以直接将newY用作NN的输出。您只能使用tf.Variable定义权重或偏差。
错误情况2。看来，tf.abs()和tf.gather()都没有tensorflow实现梯度下降。对于回归问题，均方误差通常就足够了。
在这里，我如何重写您的代码。我没有您的matlab部分，因此无法调试您的python / matlab接口：
型号：
def Model_FCL(inputX):
# Fully Connected Layer 1
fcW1 = tf.get_variable('w1', shape=[222, 1024], initializer=tf.initializer.truncated_normal())
fcb1 = tf.get_variable('b1', shape=[222], initializer=tf.initializer.truncated_normal())
# fcb1 = tf.get_variable('b1', shape=[None, 222], trainable=False, initializer=tf.constant_initializer(valueThatYouWant)) # if you want to fix your bias constant
fch1 = tf.nn.relu(tf.matmul(inputX, fcW1) + fcb1, name='relu1')
# Fully Connected Layer 2
fcW2 = tf.get_variable('w2', shape=[1024, 1024], initializer=tf.initializer.truncated_normal())
fcb2 = tf.get_variable('b2', shape=[222], initializer=tf.initializer.truncated_normal())
# fcb2 = tf.get_variable('b2', shape=[None, 222], trainable=False, initializer=tf.constant_initializer(valueThatYouWant)) # if you want to fix your bias constant
fch2 = tf.nn.relu(tf.matmul(fch1, fcW2) + fcb2, name='relu2')
# Output Layer
fcW3 = tf.get_variable('w3', shape=[1024, 222], initializer=tf.initializer.truncated_normal())
fcb3 = tf.get_variable('b3', shape=[222], initializer=tf.initializer.truncated_normal())
# fcb2 = tf.get_variable('b2', shape=[None, 222], trainable=False, initializer=tf.constant_initializer(valueThatYouWant)) # if you want to fix your bias constant
logits = tf.add(tf.matmul(fch2, fcW3), fcb3)
predictY = tf.nn.softmax(logits) #I'm not sure that it will learn if you do softmax then abs/MSE
return predictY, logits
图形：
with myGraph.as_default():
# define input data & output data 입력받기 위한 placeholder
# put None(dynamic batch size) not -1 at the first dimension so that you can change your batch size
x = tf.placeholder(tf.float32, shape=[None, 222], name='inputX') # Signal size = [1, 222]
y = tf.placeholder(tf.float32, shape=[None], name='trueY') # Float value size = [1]
...
predictY, logits = Model_FCL(x) # Predict Signal, size = [1, 222]
maxPredict = tf.argmax(predictY, 1, name='maxPredict') # Find max index of Predict Signal
tensorFreq = tf.convert_to_tensor(basicFreq, tf.float32)
newY = tf.gather(tensorFreq, maxPredict) * 60 # Find the value that corresponds to the Freq array index
loss = tf.losses.mean_squared_error(labels=y, predictions=newY) # maybe use MSE for regression problem
# loss = tf.abs(y - newY) # Calculate absolute (true Y - predict Y) #tensorflow doesn't have gradient descent implementation for tf.abs
opt = tf.train.AdamOptimizer(learning_rate=0.0001)
trainStep = opt.minimize(loss)