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InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape

I have written the following code in my Pycharm which does Fully Connect Layer (FCL) in Tensorflow. The placeholder happens invalid argument error. So I entered all the dtype, shape, and name in the placeholder, but I still get invalid argument error.
I want to make new Signal(1, 222) through FCL model.
input Signal(1, 222) => output Signal(1, 222)
maxPredict: Find the index with the highest value in the output signal.
calculate Y: Get the frequency array value corresponding to maxPredict.
loss: Use the difference between true Y and calculate Y as a loss.
loss = tf.abs(trueY - calculateY)`
Code (occur Error)
x = tf.placeholder(dtype=tf.float32, shape=[1, 222], name='inputX')
ERROR
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'inputX' with dtype float and shape [1,222]
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'inputX' with dtype float and shape [1,222]
[[{{node inputX}} = Placeholderdtype=DT_FLOAT, shape=[1,222], _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
During handling of the above exception, another exception occurred:
New Error Case
I changed my Code.
x = tf.placeholder(tf.float32, [None, 222], name='inputX')
Error Case 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 must have a shape specified: Tensor("mul:0", shape=(?,), dtype=float32)
Error Case 2
tensorFreq = tf.convert_to_tensor(basicFreq, tf.float32)
newY = tf.gather(tensorFreq, maxPredict) * 60
loss = tf.abs(y - newY)
Traceback (most recent call last):
File "D:/PycharmProject/DetectionSignal/TEST_FCL_StackOverflow.py", line 127, in
trainStep = opt.minimize(loss)
File "C:\Users\Heewony\Anaconda3\envs\TSFW_pycharm\lib\site-packages\tensorflow\python\training\optimizer.py", line 407, in minimize
([str(v) for _, v in grads_and_vars], loss))
ValueError: No gradients provided for any variable, check your graph for ops that do not support gradients, between variables [tf.Variable 'Variable:0' shape=(222, 1024) dtype=float32_ref, tf.Variable 'Variable_1:0' shape=(1024,) dtype=float32_re, ......... tf.Variable 'Variable_5:0' shape=(222,) dtype=float32_ref] and loss Tensor("Abs:0", dtype=float32).
Development environment
OS Platform and Distribution: Windows 10 x64
TensorFlow installed from: Anaconda
Tensorflow version 1.12.0:
python 3.6.7 :
Mobile device: N/A
Exact command to reproduce: N/A
GPU model and memory: NVIDIA GeForce CTX 1080 Ti
CUDA/cuDNN: 9.0/7.4
Model and Function
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
Graph
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())
Session
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()

It seems that the variable batchSignal is of a wrong type or shape. It must be a numpy array of shape exactly [1, 222]. If you want to use a batch of examples of size n × 222, the placeholder x should have a shape of [None, 222] and placeholder y shape [None].
By the way, consider using tf.layers.dense instead of explicitly initializing variables and implementing the layers yourself.

There should be two things to change.
Error Case 0. You don't need to reshape your flow between layers. You can use None at the first dimension to pass a dynamic batch size.
Error Case 1. You can use directly your newY as output of the NN. You only use tf.Variable to define weights or bias.
Error Case 2. And it seems that tensorflow doesn't have gradient descent implementation for neither tf.abs() nor tf.gather(). With a regression problem, the mean square error is often sufficient.
Herein, how I rewrite your code. I don't have your matlab part so I can't debug your python/matlab interface:
Model:
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
Graph:
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)

If you are still getting the same error even after feeding the right numpy shape and also maintaining the correct dtypes (np.int32 or np.float32) as suggested by the error message, then the following code should solve your problem:
#this code will print the list of placeholders and other variables declared in the memory which is causing your error
[n.name for n in tf.get_default_graph().as_graph_def().node]
#it will reset your declared placeholders so you can start over
tf.reset_default_graph()
This problem could also be solved by restarting the kernel repeatedly for each debug however it's not feasible.

How do I initialize LSTM weights from Numpy arrays correctly in Tensorflow?

I have the same question for tf.contrib.rnn.LSTMBlockCell and tf.contrib.cudnn_rnn.CudnnCompatibleLSTMCell:
How do I initialize the LSTM weights from numpy arrays correctly? The following code-snipped executes, but does not seem
to do what I am looking for:
train_data = np.load('mnist_train_data.npy').reshape(-1,28,28)
train_label = np.load('mnist_train_label.npy')
params = [np.random.randn(28+128, 4*128), np.zeros(4*128)]
X = tf.placeholder(tf.float32, shape=[54999, 28, 28])
y = tf.placeholder(tf.int64, None)
state = LSTMStateTuple(*(tf.zeros((54999, 128), dtype=tf.float32) for _ in range(2)))
cell = tf.contrib.rnn.LSTMBlockCell(128)
cell.build(tf.TensorShape((None, 28)))
cell.set_weights(params)
initial_weights = cell.get_weights()
print(np.array_equal(params[0], initial_weights[0]))
w1 = tf.Variable(np.random.randn(128, 10), dtype=tf.float32)
b1 = tf.Variable(np.zeros(10), dtype=tf.float32)
full_seq, current_state = tf.nn.dynamic_rnn(cell, X, initial_state=state, dtype=tf.float32)
output = tf.matmul(current_state[1], w1)
output += b1
loss = tf.losses.softmax_cross_entropy(y, output)
train_step = tf.train.AdamOptimizer(0.01).minimize(loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(1):
feed_dict = {X: train_data, y: train_label}
sess.run(train_step, feed_dict=feed_dict)
final_weights = cell.get_weights()
print(np.array_equal(initial_weights[0], final_weights[0]))
This prints out False in the first print-statement, so the numpy arrays do not actually seem to be used as weights.
Moreover, after the training session, this prints out True thus implying, that these weights are not actually updated during training.
Thanks in advance for any help on the subject.

Tensorflow throws "Dimensions must be equal, but are 100 and 0 for 'MatMul' (op: 'MatMul') with input shapes: [0,100], [0,100]."

I am trying to learn tensorflow after a tutorial, but I don't want to use mnist database, so I can learn database management in python( I am new to it, and it's a hard learning curve going from c++/java to it)
So, here is my code. I've tried printing shapes, values, and all sort of stuff, but none seemed to work. Note: if i make x of shape [0, 100] and the weights [100, 0], the error from matmul goes away, but the resul is of shape [0,0] and cannot be added to the biases. I am 100% sure it's a newbie error, but i will appreciate any help from you. Thanks in advance.
import tensorflow as tf
import pandas as pd
data = pd.read_csv('trainingData.txt', sep = "\t", header = None )
data.columns = ["in", "out"]
data_x = data.loc[: , "in"]
data_y = data.loc[: , "out"]
n_noduri_hl1 = 100
n_noduri_hl2 = 250
n_noduri_hl3 = 100
batch_size = 100
x = tf.placeholder("float", [0, 100])
y = tf.placeholder('float')
def Neural_Network(data):
# input * wheight + bias
hidden_1 = {'weight': tf.Variable(tf.random_normal([0, n_noduri_hl1])),
'biases': tf.Variable(tf.random_normal([n_noduri_hl1]))}
hidden_2 = {'weight': tf.Variable(tf.random_normal([n_noduri_hl1, n_noduri_hl2])),
'biases': tf.Variable(tf.random_normal([n_noduri_hl2]))}
hidden_3 = {'weight': tf.Variable(tf.random_normal([n_noduri_hl2, n_noduri_hl3])),
'biases': tf.Variable(tf.random_normal([n_noduri_hl3]))}
output_layer = {'weight': tf.Variable(tf.random_normal([n_noduri_hl3, 1])),
'biases': tf.Variable(tf.random_normal([1]))}
#calcul
print("data: ", data, "matmul: ", tf.matmul(data, hidden_1['weight']))
l1 = tf.add(tf.matmul(data, hidden_1['weight']), hidden_1['biases'])
l1 = tf.nn.relu(l1)
l2 = tf.add(tf.matmul(l1, hidden_2['weight']), hidden_2['biases'])
l2 = tf.nn.relu(l2)
l3 = tf.add(tf.matmul(l2, hidden_3['weight']), hidden_3['biases'])
l3 = tf.nn.relu(l3)
output = tf.matmul(l3, output_layer['weight']) + output_layer['biases']
return output
def get_next_batch(dataptr, batch_size, index):
batch = dataptr.loc[index: index+batch_size]
print(batch)
return batch
def train(x):
predictie = Neural_Network(x)
cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(logits = predictie, labels = y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
epoci = 10
index = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoca in range(epoci):
loss = 0
for _ in range(int(len(data)/batch_size)):
ep_x = get_next_batchin(data_x, batch_size, index)
ep_y = get_next_batchout(data_ybatch_size, index)
index += batch_size
_, c = sess.run([optimizer, cost], feed_dict = {x: ep_x, y: ep_y})
loss += c
print('Epoca: ', epoca, " din ", epoci, " loss: ", loss)
corect = tf.equal(tf.argmax(predictie, 1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(corect, 'float'))
print('Acuratete: ', accuracy.eval({x: data.loc[: , "in"], y: data.loc[: , "out"]}))
train(x)

Instead of 0, your placeholder should have None for the first dimension (the batch dimension) and the following dimensions should be the size of the description vector / matrix.
For example, x = tf.placeholder("float", [None, 64, 64, 3]) would be the place holder for a batch of 64 x 64 pixel RGB colour images.
When performing 2D matrix multiplication, the number of columns of the first operand must match the number of rows of the second operand. This is just how matrix multiplication is defined.

ValueError: Tensor("BN_1/moments/Squeeze:0", shape=(32, 256, 32), dtype=float32) must be from the same graph as Tensor

I'm trying to get started with TensorFlow in python, building a simple CNN with batch normalization. But when i create a new graph to run, exception happens to BN.
My key codes is as follows
**# exception here**
def batch_norm(x, beta, gamma, phase_train, scope='bn', decay=0.9, eps=1e-5):
with tf.variable_scope(scope):
batch_mean, batch_var = tf.nn.moments(x, [0], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=decay)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = tf.cond(phase_train, mean_var_with_update, lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, eps)
return normed
training code:
# start training
output = conv2d_net()
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.002).minimize(loss)
predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])
max_idx_p = tf.argmax(predict, 2)
max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
correct_pred = tf.equal(max_idx_p, max_idx_l)
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
step = 0
while True:
batch_x, batch_y = get_next_batch(64)
_, loss_ = sess.run([optimizer, loss],
feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75, train_phase: True})
print(step, loss_)
if step % 10 == 0 and step != 0:
batch_x_test, batch_y_test = get_next_batch(100)
acc = sess.run(accuracy,
feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1., train_phase: False})
print("step %s，accuracy：%s" % (step, acc))
if acc > 0.05:
# stop training and save parameters in layer
result_weights['wc1'] = weights['wc1'].eval(sess)
...
break
step += 1
Create new graph for exporting:
EXPORT_DIR = './model'
if os.path.exists(EXPORT_DIR):
shutil.rmtree(EXPORT_DIR)
g = tf.Graph()
with g.as_default():
x_2 = tf.placeholder(tf.float32, shape=[None, IMAGE_HEIGHT * IMAGE_WIDTH], name="input")
x_image = tf.reshape(x_2, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])
# fill trained parameters and create new cnn layers
WC1 = tf.constant(result_weights['wc1'], name="WC1")
...
**# crash here!!!**
CONV1 = conv2d(WC1, BC1, x_image, tf.constant(0.0, shape=[32]),
tf.random_normal(shape=[32], mean=1.0, stddev=0.02), scope='BN_1')
OUTPUT = tf.add(tf.matmul(FULL1, W_OUT), B_OUT)
OUTPUT = tf.nn.sigmoid(OUTPUT, name="output")
sess = tf.Session()
sess.run(tf.global_variables_initializer())
graph_def = g.as_graph_def()
tf.train.write_graph(graph_def, EXPORT_DIR, 'phone_model_graph.pb', as_text=True)
I create a new graph at last. The exception means it uses incorrect parameter in old training graph. How to explain it?
Thank you very much!
Log is:
I call batch_norm in fuction conv2d. It seems no tensor passed to the new graph.
def conv2d(w, b, x, tf_constant, tf_random_normal, scope, keep_p=1., phase=tf.constant(False)):
out = tf.nn.bias_add(tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME'), b)
out = batch_norm(out, tf_constant, tf_random_normal, phase, scope=scope)
out = tf.nn.relu(out)
out = tf.nn.max_pool(out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
out = tf.nn.dropout(out, keep_p)
return out

I create a new graph at last.
That's the key statement here: upon creation of a new graph one can't use any tensor from the old graph. See a detailed explanation in this question. According to the stacktrace, at least one of the tensors that is passed to the batch_norm is defined before g.as_default(), that's why tensorflow crashes. From your code snippets it's unclear how exactly the batch_norm is called, so I can't say which one.
You can check this hypothesis by printing x.graph and g and checking if these values are different. In order to avoid this problem you can either do all the work inside one graph (which is a recommended way) or define both graphs in different python scopes thus making impossible to accidentally reuse the same python variable in two graphs.

CTC loss Tensorflow, No valid path found

I'm trying to use ctc_loss in order to predict some labels, but it's the first time I use this function and I got an error.
def bi_rnn(x_bi):
# Prepare data shape to match `rnn` function requirements
# Current data input shape: (batch_size, timesteps, n_input)
# Required shape: 'timesteps' tensors list of shape (batch_size, num_input)
# Unstack to get a list of 'timesteps' tensors of shape (batch_size, num_input)
x_bi_shape = x_bi.get_shape().as_list()
x_bi = tf.unstack(x_bi, x_bi_shape[2], 2)
# Define lstm cells with tensorflow
# Forward direction cell
lstm_fw_cell = rnn.BasicLSTMCell(num_neurons, forget_bias=1.0)
# Backward direction cell
lstm_bw_cell = rnn.BasicLSTMCell(num_neurons, forget_bias=1.0)
bi_outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x_bi, dtype=tf.float32)
w_bi = init_weights([num_neurons * 2, num_classes])
b_bi = init_bias([num_classes])
bi_outputs = tf.squeeze(input=bi_outputs, axis=1)
return tf.matmul(bi_outputs, w_bi) + b_bi
def sparse_tuple_from(sequences, dtype=np.int32):
"""Create a sparse representention of x.
Args:
sequences: a list of lists of type dtype where each element is a sequence
Returns:
A tuple with (indices, values, shape)
"""
indices = []
values = []
for n, seq in enumerate(sequences):
indices.extend(zip([n] * len(seq), range(len(seq))))
values.extend(seq)
indices = np.asarray(indices, dtype=np.int64)
values = np.asarray(values, dtype=dtype)
shape = np.asarray([len(sequences), np.asarray(indices).max(0)[1] + 1], dtype=np.int64)
return indices, values, shape
bi_output_1 = bi_rnn(vectors)
targets = tf.sparse_placeholder(tf.int32, name="targets_sparse_tensor")
bi_output_1_shape = bi_output_1.get_shape().as_list()
global_step = tf.Variable(0, trainable=False)
bi_output_1 = tf.reshape(bi_output_1, [bi_output_1_shape[0], batch_size, bi_output_1_shape[1]])
# 1d array of size [batch_size]
seq_len = tf.placeholder(tf.int32, [None])
loss = tf.nn.ctc_loss(labels=targets, inputs=bi_output_1, sequence_length=seq_len)
cost = tf.reduce_mean(loss)
optimizer = tf.train.MomentumOptimizer(learning_rate=0.001, momentum=0.9).minimize(cost, global_step=global_step)
here is how I train the net:
with tf.Session() as sess:
sess.run(init)
for i in range(1, epochs):
loss = 0
start = 0
for cont in range(int(n_samples / batch_size)):
batch_x, batch_y = feature_set.next_batch_training_set(batch_size, start)
batch_y = sparse_tuple_from(batch_y)
seq_len_training = np.ones(1) * 7
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, targets: batch_y, seq_len: seq_len_training})
loss += c
print('Epoch:', i, ' loss:', loss)
with:
batch_size = 1
bi_output_1 = Tensor("Reshape:0", shape=(25, 1, 37), dtype=float32)
batch_x has shape : (1, 20, 100, 1)
batch_y has shape : (1, 7) (before the function sparse_tuple_from())
I got this error:
2017-11-23 18:50:14.033220: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\util\ctc\ctc_loss_calculator.cc:144] No valid path found.
and I don't know how to solve this. I tried to follow this example, but I got no help.