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TensorFlow - tensorflow.python.framework.errors_impl.FailedPreconditionError

I tried to update the following code from TF 1.7 to TF 1.14 :
def build(self, input_shape):
self.max_length = input_shape[1]
self.word_mapping = [x[1] for x in sorted(self.idx2word.items(), key=lambda x: x[0])]
self.lookup_table = tf.contrib.lookup.index_to_string_table_from_tensor(self.word_mapping, default_value="<UNK>")
self.lookup_table.init.run(session=K.get_session())
self.elmo_model = hub.Module("https://tfhub.dev/google/elmo/2", trainable=self.trainable)
super(ELMoEmbedding, self).build(input_shape)
def call(self, x):
x = tf.cast(x, dtype=tf.int64)
sequence_lengths = tf.cast(tf.count_nonzero(x, axis=1), dtype=tf.int32)
strings = self.lookup_table.lookup(x)
inputs = {
"tokens": strings,
"sequence_len": sequence_lengths
}
return self.elmo_model(inputs, signature="tokens", as_dict=True)[self.output_mode]
When I fit my model, the following line
self.lookup_table.init.run(session=K.get_session())
generates the following error:
'StaticHashTableV1' object has no attribute 'init'. So I modified the code following the TF r1.14 doc
def build(self, input_shape):
self.max_length = input_shape[1]
self.word_mapping = [x[1] for x in sorted(self.idx2word.items(), key=lambda x: x[0])]
#self.lookup_table = tf.contrib.lookup.index_to_string_table_from_tensor(self.word_mapping, default_value="<UNK>")
self.lookup_table = tf.contrib.lookup.index_to_string_table_from_tensor(self.word_mapping,
default_value='<UNK>',
name=None
)
with tf.Session() as session:
session.run(tf.compat.v1.tables_initializer)
# Initialize the variables (i.e. assign their default value)
self.elmo_model = hub.Module("https://tfhub.dev/google/elmo/2", trainable=self.trainable)
super(ELMoEmbedding, self).build(input_shape)
def call(self, x):
x = tf.cast(x, dtype=tf.int64)
sequence_lengths = tf.cast(tf.count_nonzero(x, axis=1), dtype=tf.int32)
strings = self.lookup_table.lookup(x)
inputs = {
"tokens": strings,
"sequence_len": sequence_lengths
}
return self.elmo_model(inputs, signature="tokens", as_dict=True)[self.output_mode]
and I get this error:
Traceback (most recent call last):
File "main.py", line 46, in <module>
protest.train(str(args[0]), dirModel, typeCorpus)
File "/home/???/clef_2019/protest.py", line 40, in train
model = classification.runTrain(dataFrame)
File "/home/???/clef_2019/classification.py", line 97, in runTrain
model = self.runAlgo(dataFrame, column)
File "/home/???/clef_2019/classification.py", line 328, in runAlgo
model.fit(xtrain, y, batch_size=8, epochs = 1)
File "/usr/local/lib/python3.6/dist-packages/kashgari/tasks/base_model.py", line 324, in fit
**fit_kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 1433, in fit_generator
steps_name='steps_per_epoch')
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_generator.py", line 264, in model_iteration
batch_outs = batch_function(*batch_data)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 1175, in train_on_batch
outputs = self.train_function(ins) # pylint: disable=not-callable
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 3292, in __call__
run_metadata=self.run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1458, in __call__
run_metadata_ptr)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Error while reading resource variable module/bilm/CNN/b_cnn_2 from Container: localhost. This could mean that the variable was uninitialized. Not found: Resource localhost/module/bilm/CNN/b_cnn_2/N10tensorflow3VarE does not exist.
[[{{node el_mo_embedding/module_apply_tokens/bilm/CNN_1/add_2/ReadVariableOp}}]]
Thx for your help :)

On
with tf.Session() as session:
session.run(tf.compat.v1.tables_initializer)
The session stops to exist when you get out of scope.
You can replace it with
tf.compat.v1.tables_initializer().run(session=K.get_session())
I think that should do the trick.

Error "Kernel already used" when using trained RNN to make prediction

Thanks for looking into this question!
I am trying to train an LSTM network which predicts the next 5-day stock prices based on past 30-day stock prices. I have trained the model based on 265 samples. The variables are defined as follow:
# Variables
x = tf.placeholder("float", [265, 30])
y = tf.placeholder("float", [265, 5])
weights = {
'out': tf.Variable(tf.random_normal([n_hidden, y_size]))
}
biases = {
'out': tf.Variable(tf.random_normal([y_size]))
}
and the model looks as below:
# Define RNN architecture
def RNN(x, weights, biases):
x_size = 30
x = tf.reshape(x, [-1, x_size])
x = tf.split(x, x_size, 1)
rnn_cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(n_hidden), rnn.BasicLSTMCell(n_hidden)])
outputs, states = rnn.static_rnn(rnn_cell, x, dtype = tf.float32)
return tf.matmul(outputs[-1], weights['out'] + biases['out'])
Then, I attempt to use the trained model to predict as follow:
y_pred = RNN(x_input, trained_weights, trained_biases)
in which x_input has a dimension (1x30). In gave me a list of error which I could not understand:
ValueError: Variable rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:
File "C:\Program Files\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1654, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
File "C:\Program Files\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 3290, in create_op
op_def=op_def)
File "C:\Program Files\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
Traceback (most recent call last):
File "C:\Users\teh.khoonkheng\Desktop\Others\Personal working folder\14. Projects\1. Oracle\Python\RNN_stock_01.py", line 135, in <module>
y_test = RNN(x_test, trained_weights, trained_biases)
File "C:\Users\teh.khoonkheng\Desktop\Others\Personal working folder\14. Projects\1. Oracle\Python\RNN_stock_01.py", line 81, in RNN
outputs, states = rnn.static_rnn(rnn_cell, x, dtype = tf.float32)
File "C:\Program Files\Python35\lib\site-packages\tensorflow\python\ops\rnn.py", line 1330, in static_rnn
(output, state) = call_cell()
File "C:\Program Files\Python35\lib\site-packages\tensorflow\python\ops\rnn.py", line 1317, in <lambda>
call_cell = lambda: cell(input_, state)
File "C:\Program Files\Python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 191, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "C:\Program Files\Python35\lib\site-packages\tensorflow\python\layers\base.py", line 714, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\Program Files\Python35\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 1242, in call
cur_inp, new_state = cell(cur_inp, cur_state)
I was wondering if I have misunderstood how static_rnn works. Have I set up the model incorrectly? And how should I use the trained RNN to make prediction?
Thanks for your help!

Like the error says, you need to mention reuse=True such that the learned states can be used later for prediction. Do this:
rnn_cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(n_hidden,reuse=tf.AUTO_REUSE), rnn.BasicLSTMCell(n_hidden,reuse=tf.AUTO_REUSE)])
Also, this model looks wrong because, while you're using the trained weights and biases, but you're not using the trained LSTMcells. For new inputs, you're defining new LSTMcells.

Nerual Network Building Running into Error

I am trying to build this LSTM network and I ran into this error constantly. I looked it up on Google and am still not sure what is going on. I have tried adding this:with tf.variable_scope("cell1"). Still not working. Any help is greatly appreciated.
Here is the code of the main LSTM structure
def lstm_structure(self):
train_dataset, train_labels, valid_dataset, valid_labels, test_dataset, test_labels = self.data_preprocessing(
'SSE_Composite_Index.csv')
graph=tf.Graph()
with graph.as_default():
'''Placeholders'''
X=tf.placeholder(tf.float32,shape=[None,self.num_steps,self.input_size])
y=tf.placeholder(tf.float32,shape=[None,self.num_classes])
valid=tf.constant(valid_dataset)
test=tf.constant(test_dataset)
'''Weights'''
weights={'in':tf.Variable(tf.random_normal([self.input_size,self.num_neurons])),'out':tf.Variable(tf.random_normal([self.num_neurons,self.num_classes]))}
'''Biases'''
biases={'in':tf.Variable(tf.zeros(shape=[self.num_neurons,])),'out':tf.Variable(tf.zeros(shape=[self.num_classes,]))}
def lstm(X, weights, biases, reuse=True
):
'''from input to cell'''
with tf.variable_scope("foo") as f:
if reuse:
f.reuse_variables()
X = tf.reshape(X, shape=[-1, self.input_size])
X_in = tf.matmul(X, weights['in']) + biases['in']
X_in = tf.reshape(X_in, shape=[-1, self.num_steps, self.num_neurons])
'''cell'''
cell_ = tf.contrib.rnn.BasicLSTMCell(self.num_neurons, forget_bias=1, state_is_tuple=True)
_init_state = cell_.zero_state(self.batch_size, dtype=tf.float32)
outputs, states = tf.nn.dynamic_rnn(cell_, X_in, initial_state=_init_state, time_major=False, dtype=tf.float32)
'''from cell to output'''
results = tf.matmul(states[1], weights['out']) + biases['out']
return results
logits=lstm(X,weights,biases,False)
loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(loss)
train_prediction=tf.nn.softmax(logits=logits)
valid_prediction=tf.nn.softmax(lstm(valid,weights,biases,True))
test_prediction=tf.nn.softmax(lstm(test,weights,biases,True))
with tf.Session(graph=graph) as session:
tf.initialize_all_variables().run()
print('Initialised')
loss_list = []
for step in range(self.num_epochs):
if step==0:
i = 0
while i < len(train_dataset):
start = i
end = i + self.batch_size
batch_data = train_dataset[start:end, :]
batch_label = train_labels[start:end, :]
i += self.batch_size
a, b, prediction = session.run([optimizer, loss, train_prediction],
feed_dict={X: batch_data, y: batch_label})
loss_list.append(b)
else:
i = 0
while i < len(train_dataset):
start = i
end = i + self.batch_size
batch_data = train_dataset[start:end, :]
batch_label = train_labels[start:end, :]
i += self.batch_size
a, b, prediction = session.run([optimizer, loss, train_prediction],
feed_dict={X: batch_data, y: batch_label})
loss_list.append(b)
if step % 10 == 0:
print('Step', step, 'Loss', b)
print('Training Accuracy', self.accuracy(prediction, batch_label), '%')
print('Validation Accuracy',
self.accuracy(valid_prediction.eval(), valid_labels), '%')
print('test Accuracy', self.accuracy(test_prediction.eval(), test_labels), '%')
print('Finished')
The error message that I am getting is:
Traceback (most recent call last):
File "C:/Users/LiXin/PycharmProjects/PythonProjects/LSTM_CLASSIFIER.py", line 114, in <module>
lstm.LSTM_structure()
File "C:/Users/LiXin/PycharmProjects/PythonProjects/LSTM_CLASSIFIER.py", line 87, in LSTM_structure
valid_prediction=tf.nn.softmax(LSTM(valid,weights,biases))
File "C:/Users/LiXin/PycharmProjects/PythonProjects/LSTM_CLASSIFIER.py", line 75, in LSTM
outputs,states=tf.nn.dynamic_rnn(cell_,X_in,initial_state=_init_state,time_major=False,dtype=tf.float32)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\rnn.py", line 614, in dynamic_rnn
dtype=dtype)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\rnn.py", line 777, in _dynamic_rnn_loop
swap_memory=swap_memory)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2816, in while_loop
result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2640, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2590, in _BuildLoop
body_result = body(*packed_vars_for_body)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\rnn.py", line 762, in _time_step
(output, new_state) = call_cell()
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\rnn.py", line 748, in <lambda>
call_cell = lambda: cell(input_t, state)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 183, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\layers\base.py", line 575, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 438, in call
self._linear = _Linear([inputs, h], 4 * self._num_units, True)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 1171, in __init__
initializer=kernel_initializer)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 1203, in get_variable
constraint=constraint)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 1092, in get_variable
constraint=constraint)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 417, in get_variable
return custom_getter(**custom_getter_kwargs)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 186, in _rnn_get_variable
variable = getter(*args, **kwargs)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 394, in _true_getter
use_resource=use_resource, constraint=constraint)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 742, in _get_single_variable
name, "".join(traceback.format_list(tb))))
ValueError: Variable rnn/basic_lstm_cell/kernel already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\framework\ops.py", line 1470, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\framework\ops.py", line 2956, in create_op
op_def=op_def)
File "C:\Users\LiXin\PycharmProjects\PythonProjects\venv\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
The following code works, however.
def RNN_neural_network_model(x):
layer={'weights':tf.Variable(tf.random_normal([rnn_size,n_classes])),
'biases':tf.Variable(tf.random_normal([n_classes]))}
x=tf.transpose(x,[1,0,2])
x=tf.reshape(x,[-1,chunk_size])
x=tf.split(x,n_chunks,0)
lstm_cell=tf.contrib.rnn.BasicLSTMCell(rnn_size,state_is_tuple=True)
# stacked_lstm = tf.contrib.rnn.MultiRNNCell(
# [lstmcell(rnn_size) for _ in range(num_layers)])
outputs, states=rnn.static_rnn(lstm_cell,x,dtype=tf.float32)
output = tf.matmul(outputs[-1], layer['weights'])+ layer['biases']
return output

You should wrap your LSTM definition inside a variable scope and then reuse it for validation and testing. Try the following
def LSTM(X,weights,biases, reuse=reuse):
'''from input to cell'''
with tf.variable_scope("foo") as f:
if reuse:
f.reuse_variables()
X=tf.reshape(X,shape=[-1,self.input_size])
X_in=tf.matmul(X,weights['in'])+biases['in']
X_in=tf.reshape(X_in,shape=[-1,self.num_steps,self.num_neurons])
'''cell'''
cell_=tf.contrib.rnn.BasicLSTMCell(self.num_neurons,forget_bias=1,state_is_tuple=True)
_init_state=cell_.zero_state(self.batch_size,dtype=tf.float32)
outputs,states=tf.nn.dynamic_rnn(cell_,X_in,initial_state=_init_state,time_major=False,dtype=tf.float32)
'''from cell to output'''
results=tf.matmul(states[1],weights['out'])+biases['out']
return results
Change your training, testing code as below
logits=LSTM(X,weights,biases,False)
loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(loss)
train_prediction=tf.nn.softmax(logits=logits)
valid_prediction=tf.nn.softmax(LSTM(valid,weights,biases, True))
test_prediction=tf.nn.softmax(LSTM(test,weights,biases, True))

How to reuse slim.arg_scope in tensorFlow?

I'm trying to load inception_resnet_v2_2016_08_30.ckpt file and do testing.
The code works well with single image (entering oneFile() function only once).
If I call oneFile() function twice, the following error occur:
ValueError: Variable InceptionResnetV2/Conv2d_1a_3x3/weights already
exists, disallowed. Did you mean to set reuse=True in VarScope?
Originally defined at:
I found related solution on Sharing Variables
If tf.variable_scope meet the same problem, could call scope.reuse_variables() to resolve this problem.
But I can't find the slim.arg_scope version to reuse the scope.
def oneFile(filepath):
imgPath = filepath
testImage_string = tf.gfile.FastGFile(imgPath, 'rb').read()
testImage = tf.image.decode_jpeg(testImage_string, channels=3)
processed_image = inception_preprocessing.preprocess_image(testImage, image_size, image_size, is_training=False)
processed_images = tf.expand_dims(processed_image, 0)
# Create the model, use the default arg scope to configure the batch norm parameters.
with slim.arg_scope(inception_resnet_v2_arg_scope()):
#logits, end_points = inception_resnet_v2(images, num_classes = dataset.num_classes, is_training = False)
logits, _ = inception_resnet_v2(processed_images, num_classes=16, is_training=False)
probabilities = tf.nn.softmax(logits)
init_fn = slim.assign_from_checkpoint_fn(
checkpoint_file,
slim.get_model_variables(model_name))
with tf.Session() as sess:
init_fn(sess)
np_image, probabilities = sess.run([processed_images, probabilities])
probabilities = probabilities[0, 0:]
sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x: x[1])]
#print(probabilities)
print(probabilities.argmax(axis=0))
#names = imagenet.create_readable_names_for_imagenet_labels()
#for i in range(15):
# index = sorted_inds[i]
# print((probabilities[index], names[index]))
def main():
for image_file in os.listdir(dataset_dir):
try:
image_type = imghdr.what(os.path.join(dataset_dir, image_file))
if not image_type:
continue
except IsADirectoryError:
continue
#image = Image.open(os.path.join(dataset_dir, image_file))
filepath = os.path.join(dataset_dir, image_file)
oneFile(filepath)
inception_resnet_v2_arg_scope
def inception_resnet_v2_arg_scope(weight_decay=0.00004,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001):
"""Yields the scope with the default parameters for inception_resnet_v2.
Args:
weight_decay: the weight decay for weights variables.
batch_norm_decay: decay for the moving average of batch_norm momentums.
batch_norm_epsilon: small float added to variance to avoid dividing by zero.
Returns:
a arg_scope with the parameters needed for inception_resnet_v2.
"""
# Set weight_decay for weights in conv2d and fully_connected layers.
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay),
biases_regularizer=slim.l2_regularizer(weight_decay)):
batch_norm_params = {
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
}
# Set activation_fn and parameters for batch_norm.
with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params) as scope:
return scope
Complete error message:
./data/test/teeth/1/7070.jpg Traceback (most recent call last): File
"testing.py", line 111, in
main() File "testing.py", line 106, in main
cal(processed_images) File "testing.py", line 67, in cal
logits, _ = inception_resnet_v2(processed_images, num_classes=16, is_training=False) File
"/notebooks/transfer_learning_tutorial/inception_resnet_v2.py", line
123, in inception_resnet_v2
scope='Conv2d_1a_3x3') File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py",
line 181, in func_with_args
return func(*args, **current_args) File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/layers/python/layers/layers.py",
line 918, in convolution
outputs = layer.apply(inputs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py",
line 320, in apply
return self.call(inputs, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py",
line 286, in call
self.build(input_shapes[0]) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/convolutional.py",
line 138, in build
dtype=self.dtype) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py",
line 1049, in get_variable
use_resource=use_resource, custom_getter=custom_getter) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py",
line 948, in get_variable
use_resource=use_resource, custom_getter=custom_getter) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py",
line 349, in get_variable
validate_shape=validate_shape, use_resource=use_resource) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py",
line 1389, in wrapped_custom_getter
*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py",
line 275, in variable_getter
variable_getter=functools.partial(getter, **kwargs)) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py",
line 228, in _add_variable
trainable=trainable and self.trainable) File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/layers/python/layers/layers.py",
line 1334, in layer_variable_getter
return _model_variable_getter(getter, *args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/layers/python/layers/layers.py",
line 1326, in _model_variable_getter
custom_getter=getter, use_resource=use_resource) File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py",
line 181, in func_with_args
return func(*args, **current_args) File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/variables.py",
line 262, in model_variable
use_resource=use_resource) File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py",
line 181, in func_with_args
return func(*args, **current_args) File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/variables.py",
line 217, in variable
use_resource=use_resource) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py",
line 341, in _true_getter
use_resource=use_resource) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py",
line 653, in _get_single_variable
name, "".join(traceback.format_list(tb)))) ValueError: Variable InceptionResnetV2/Conv2d_1a_3x3/weights already exists, disallowed.
Did you mean to set reuse=True in VarScope? Originally defined at:
File
"/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/variables.py",
line 217, in variable
use_resource=use_resource) File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py",
line 181, in func_with_args
return func(*args, **current_args) File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/variables.py",
line 262, in model_variable
use_resource=use_resource)

It seems like tf.reset_default_graph() before processing each image in your oneFile() function will solve this problem, as I encountered the same issue on a very similar example code. My understanding is that once you feed the image to the neural network (NN), because of the variable scope concept TensorFlow uses, it needs to be told that the variables can be reused before you can apply the NN to another image.

My guess would be that you specified the same scope for multiple variables in the graph. This error occurs when tensorflow finds multiple variables under the same scope which is irrespective of the next image or the next batch. When you create the graph, you should create it thinking about one image or batch only. If everything works well with the first batch or first image, tensorflow will take care of the next iterations including the scoping.
So check all the scopes in your model file. I am pretty sure you used the same name twice.

Tensorflow - Reusing model InvalidArgumentError

I have issues using an exported tensorflow model. It doesn't allow me to evaluate the dataset I provided it with. If I run the evaluation in the same session as the training, there are no issues, however, that defeats the purpose of saving the model, if I have to retrain my model just to test with another dataset. The python file for generating the model is as such:
x = tf.placeholder(tf.float32, shape=[None, 1024], name = "x")
y_ = tf.placeholder(tf.float32, shape=[None, 10], name = "y_")
#===Model===
#Train
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name= "accuracy")
#Create Saver
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
for i in range(40000):
batch = shvn_data.nextbatch(100)
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 %f"%(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
#Save
saver.save(sess,'svhn_model1')
I saved input variables x and y_, to be fed through function 'accuracy', so that I can run accuracy.eval() to obtain the accuracy of prediction. I evaluated the dataset in batches of 100 images, then summed the final prediction. The python file to evaluate the model in another session is as such:
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config = config)
shvn_data = DataLoader()
saver = tf.train.import_meta_graph('svhn_model1.meta')
saver.restore(sess,tf.train.latest_checkpoint('./'))
#sess.run(tf.global_variables_initializer())
#Variables to use with model
graph = tf.get_default_graph()
x = graph.get_tensor_by_name("x:0")
y_ = graph.get_tensor_by_name("y_:0")
accuracy = graph.get_tensor_by_name("accuracy:0")
keep_prob = tf.placeholder(tf.float32)
img_whole = np.reshape(shvn_data.test_images,(-1,1024))
batch_whole = np.asarray(shvn_data.test_label.eval(), dtype = np.float32)
total_accuracy = 0
test_count = shvn_data.TEST_COUNT
batch_size = 100
steps = int(math.ceil(test_count/float(batch_size)))
for j in range(steps):
start = j*batch_size
if (j+1)*batch_size > shvn_data.TEST_COUNT:
end = test_count
else:
end = (j+1)*batch_size
img_batch = img_whole[start:end]
label_batch = batch_whole[start:end]
batch_accuracy = accuracy.eval(session = sess, feed_dict={ x: img_batch, y_: label_batch, keep_prob: 1.0}) #ISSUE LIES HERE
print("Test batch %d:%d accuracy %g"%(start,end,batch_accuracy))
total_accuracy += batch_accuracy
print ("Total Accuracy: %f" %(total_accuracy/steps))
The error is as follows.
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1052, in _do_call
raise type(e)(node_def, op, message)
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
Caused by op u'Placeholder', defined at:
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/spyder/utils/ipython/start_kernel.py", line 227, in <module>
main()
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/spyder/utils/ipython/start_kernel.py", line 223, in main
kernel.start()
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 474, in start
ioloop.IOLoop.instance().start()
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tornado/ioloop.py", line 887, in start
handler_func(fd_obj, events)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher
return self.dispatch_shell(stream, msg)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
handler(stream, idents, msg)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 390, in execute_request
user_expressions, allow_stdin)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 501, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2827, in run_ast_nodes
if self.run_code(code, result):
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-1-33e4fce19d34>", line 1, in <module>
runfile('/home/lwenyao/Desktop/Python/Import_Model.py', wdir='/home/lwenyao/Desktop/Python')
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 866, in runfile
execfile(filename, namespace)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 94, in execfile
builtins.execfile(filename, *where)
File "/home/lwenyao/Desktop/Python/Import_Model.py", line 63, in <module>
saver = tf.train.import_meta_graph('svhn_model1.meta')
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1595, in import_meta_graph
**kwargs)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/meta_graph.py", line 499, in import_scoped_meta_graph
producer_op_list=producer_op_list)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/importer.py", line 308, in import_graph_def
op_def=op_def)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/lwenyao/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1228, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
As previously mentioned, evaluation has no issues if I run it in the same session when training the model. The only changes that I made were i added the argument session = sess each time I called .eval() when using the imported model. Sorry for the long post!

Alright, it appears that the error was caused from attempting to create and use another keep_prob variable in the test script, after importing the model. I.e. I created keep_prob = tf.placeholder(tf.float32,) in the training file. However,accuracy.eval() in the testing file was trying to look for keep_prob specifically from the model. I created another keep_prob = tf.placeholder(tf.float32,) in testing file, thinking it would be the same, but it was not.
I modified my code in the training file by adding the label:
keep_prob = tf.placeholder(tf.float32, name="keep_prob")
and in my testing file, called for the model's variable:
#Variables to use with model
graph = tf.get_default_graph()
x = graph.get_tensor_by_name("x:0")
y_ = graph.get_tensor_by_name("y_:0")
keep_prob = graph.get_tensor_by_name("keep_prob:0")#Changed this
accuracy = graph.get_tensor_by_name("accuracy:0")
And now it works fine. My code is modified from Deep MNIST for Experts from tensorflow.