I have a straightforward model developed in Keras:
....
model = Model(input, output)
model.compile(optimizer='adam', loss='categorical_crossentropy')
graph = tf.compat.v1.get_default_graph()
graph.finalize()
history = model.fit(X, y, epochs=30)
Since I'm dealing with some memory leak problems, it seemed like a good idea to finalize the graph to prevent the mentioned issue. But when I do, I get an exception RuntimeError: Graph is finalized and cannot be modified.:
Traceback (most recent call last):
File "./train.py", line 43, in <module>
history = model.fit(X, y, epochs=30)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 780, in fit
steps_name='steps_per_epoch')
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_arrays.py", line 157, in model_iteration
f = _make_execution_function(model, mode)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_arrays.py", line 532, in _make_execution_function
return model._make_execution_function(mode)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 2276, in _make_execution_function
self._make_train_function()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 2212, in _make_train_function
if not isinstance(K.symbolic_learning_phase(), int):
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 299, in symbolic_learning_phase
False, shape=(), name='keras_learning_phase')
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py", line 2159, in placeholder_with_default
return gen_array_ops.placeholder_with_default(input, shape, name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 6406, in placeholder_with_default
"PlaceholderWithDefault", input=input, shape=shape, name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py", line 527, in _apply_op_helper
preferred_dtype=default_dtype)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 1224, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py", line 305, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py", line 246, in constant
allow_broadcast=True)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py", line 290, in _constant_impl
name=name).outputs[0]
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 3588, in create_op
self._check_not_finalized()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py", line 3225, in _check_not_finalized
raise RuntimeError("Graph is finalized and cannot be modified.")
RuntimeError: Graph is finalized and cannot be modified.
There's nothing custom in this model, all the layers are from Keras library. And I'm using Tensorflow 1.14 and the Keras that comes with it (tensorflow.keras).
My question is, what are my options here? How can I pinpoint the reason for graph change? Or maybe I'm finalizing the graph wrong!?
[UPDATE]
To make sure that the problem is not with my setup and model, I followed the example provided in Tensorflow docs (follow the Colab link). I just added the two lines of code to finalize the graph just before calling the fit method. And I faced the same error. So my question stands, how do you finalize a model in Keras?
Related
I'm trying debug my trained Faster R-CNN model using Tensorflow Object Detection API and I want to visualize the proposal regions of RPN on an image. Can anyone tell me how to do it?
I found a post here but it hasn't been answered. I tried to export the model using exporter_main_v2.py with only the RPN head as said here and this is the massage when I deleted the second_stage.
Traceback (most recent call last):
File "exporter_main_v2.py", line 165, in <module>
app.run(main)
File "E:\Anaconda\envs\TFOD\lib\site-packages\absl\app.py", line 312, in run
_run_main(main, args)
File "E:\Anaconda\envs\TFOD\lib\site-packages\absl\app.py", line 258, in _run_main
sys.exit(main(argv))
File "exporter_main_v2.py", line 158, in main
exporter_lib_v2.export_inference_graph(
File "E:\Anaconda\envs\TFOD\lib\site-packages\object_detection\exporter_lib_v2.py", line 245, in export_inference_graph
detection_model = INPUT_BUILDER_UTIL_MAP['model_build'](
File "E:\Anaconda\envs\TFOD\lib\site-packages\object_detection\builders\model_builder.py", line 1226, in build
return build_func(getattr(model_config, meta_architecture), is_training,
File "E:\Anaconda\envs\TFOD\lib\site-packages\object_detection\builders\model_builder.py", line 665, in _build_faster_rcnn_model
second_stage_box_predictor = box_predictor_builder.build_keras(
File "E:\Anaconda\envs\TFOD\lib\site-packages\object_detection\builders\box_predictor_builder.py", line 991, in build_keras
raise ValueError(
ValueError: Unknown box predictor for Keras: None
I tried again to export the model without deleting the second_stage. And this is the message I got
INFO:tensorflow:depth of additional conv before box predictor: 0
I0802 20:55:13.930429 1996 convolutional_keras_box_predictor.py:153] depth of additional conv before box predictor: 0
Traceback (most recent call last):
File "exporter_main_v2.py", line 165, in <module>
app.run(main)
File "E:\Anaconda\envs\TFOD\lib\site-packages\absl\app.py", line 312, in run
_run_main(main, args)
File "E:\Anaconda\envs\TFOD\lib\site-packages\absl\app.py", line 258, in _run_main
sys.exit(main(argv))
File "exporter_main_v2.py", line 158, in main
exporter_lib_v2.export_inference_graph(
File "E:\Anaconda\envs\TFOD\lib\site-packages\object_detection\exporter_lib_v2.py", line 271, in export_inference_graph
concrete_function = detection_module.__call__.get_concrete_function()
File "E:\Anaconda\envs\TFOD\lib\site-packages\tensorflow\python\eager\def_function.py", line 1299, in get_concrete_function
concrete = self._get_concrete_function_garbage_collected(*args, **kwargs)
File "E:\Anaconda\envs\TFOD\lib\site-packages\tensorflow\python\eager\def_function.py", line 1205, in _get_concrete_function_garbage_collected
self._initialize(args, kwargs, add_initializers_to=initializers)
File "E:\Anaconda\envs\TFOD\lib\site-packages\tensorflow\python\eager\def_function.py", line 725, in _initialize
self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
File "E:\Anaconda\envs\TFOD\lib\site-packages\tensorflow\python\eager\function.py", line 2969, in _get_concrete_function_internal_garbage_collected
graph_function, _ = self._maybe_define_function(args, kwargs)
File "E:\Anaconda\envs\TFOD\lib\site-packages\tensorflow\python\eager\function.py", line 3361, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "E:\Anaconda\envs\TFOD\lib\site-packages\tensorflow\python\eager\function.py", line 3196, in _create_graph_function
func_graph_module.func_graph_from_py_func(
File "E:\Anaconda\envs\TFOD\lib\site-packages\tensorflow\python\framework\func_graph.py", line 990, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "E:\Anaconda\envs\TFOD\lib\site-packages\tensorflow\python\eager\def_function.py", line 634, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
File "E:\Anaconda\envs\TFOD\lib\site-packages\tensorflow\python\framework\func_graph.py", line 977, in wrapper
raise e.ag_error_metadata.to_exception(e)
tensorflow.python.autograph.pyct.error_utils.KeyError: in user code:
E:\Anaconda\envs\TFOD\lib\site-packages\object_detection\exporter_lib_v2.py:163 call_func *
return self._run_inference_on_images(images, true_shapes, **kwargs)
E:\Anaconda\envs\TFOD\lib\site-packages\object_detection\exporter_lib_v2.py:129 _run_inference_on_images *
detections[classes_field] = (
KeyError: 'detection_classes'
Found the solution!
In the config file add number_of_stages: 1
Instead of using exporter_main_v2.pyI write code that builds the model from the checkpoint file
# Load pipeline config and build a detection model
configs = config_util.get_configs_from_pipeline_file(path_to_config)
model_config = configs['model']
detection_model = model_builder.build(model_config=model_config, is_training=False)
# Restore checkpoint
ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
ckpt.restore(os.path.join(path_to_ckpt, 'ckpt-0')).expect_partial()
Then I feed the image I need to inspect to the model, then I use object_detection.utils.visualization_utils.visualize_boxes_and_labels_on_image_array to inspect the boxes
I wanted to save a model to do some predictions on specific pictures. Here is my serving function:
def _serving_input_receiver_fn():
# Note: only handles one image at a time
feat = tf.placeholder(tf.float32, shape=[None, 120, 50, 1])
return tf.estimator.export.TensorServingInputReceiver(features=feat, receiver_tensors=feat)
and here is where I export the model:
export_dir_base = os.path.join(FLAGS.model_dir, 'export')
export_dir = estimator.export_savedmodel(
export_dir_base, _serving_input_receiver_fn)
But I get the following error:
ValueError: Both labels and logits must be provided.
Now this Error I don't understand since the Serving stuff should just create a placeholder so I can later put some images through the placeholder to make predictions on the saved model?
Here is the whole traceback:
Traceback (most recent call last):
File "/home/cezary/models/official/mnist/mnist_tpu.py", line 222, in <module>
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "/home/cezary/models/official/mnist/mnist_tpu.py", line 206, in main
export_dir_base, _serving_input_receiver_fn)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 650, in export_savedmodel
mode=model_fn_lib.ModeKeys.PREDICT)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 703, in _export_saved_model_for_mode
strip_default_attrs=strip_default_attrs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 811, in _export_all_saved_models
mode=model_fn_lib.ModeKeys.PREDICT)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 1971, in _add_meta_graph_for_mode
mode=mode)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 879, in _add_meta_graph_for_mode
config=self.config)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 1992, in _call_model_fn
features, labels, mode, config)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1107, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 2203, in _model_fn
features, labels, is_export_mode=is_export_mode)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 1131, in call_without_tpu
return self._call_model_fn(features, labels, is_export_mode=is_export_mode)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py", line 1337, in _call_model_fn
estimator_spec = self._model_fn(features=features, **kwargs)
File "/home/cezary/models/official/mnist/mnist_tpu.py", line 95, in model_fn
cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_impl.py", line 156, in sigmoid_cross_entropy_with_logits
labels, logits)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_ops.py", line 1777, in _ensure_xent_args
raise ValueError("Both labels and logits must be provided.")
ValueError: Both labels and logits must be provided.
Nevermind the mnist naming, I just used the structure of the code, but didn't rename it.
Thanks for any help!
(I can't comment with a brand new account.) I was able to replicate your error by setting features and receiver_tensors to have the same value, but I don't think that your __serving_input_receiver_fn is implemented correctly. Can you follow the example here?
I first constructed an RBM and tested it on a set of data, it worked well. Then I wrote a DBN with stacked RBM and trained it with the same set of data. The program stopped with the following error when it tried to train the second RBM.
Traceback (most recent call last):
File "D:\Python\DL_DG\analysis\debug\debug_01_ppi.py", line 44, in <module>
ppi_dbn.fit(ppi_in)
File "D:/Python/DL_DG/Model\dbn_test.py", line 95, in fit
rbm.fit(input_data)
File "D:/Python/DL_DG/Model\rbm_test.py", line 295, in fit
self.partial_fit(batch_x, b, e)
File "D:/Python/DL_DG/Model\rbm_test.py", line 188, in partial_fit
feed_dict={self.x: batch_x})
File "C:\Users\pil562\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 895, in run
run_metadata_ptr)
File "C:\Users\pil562\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1124, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\pil562\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1321, in _do_run
options, run_metadata)
File "C:\Users\pil562\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1340, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'input/x' with dtype float and shape [?,128]
[[Node: input/x = Placeholder[dtype=DT_FLOAT, shape=[?,128], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'input/x', defined at:
File "<string>", line 1, in <module>
File "C:\Users\pil562\AppData\Local\Programs\Python\Python36\lib\idlelib\run.py", line 142, in main
ret = method(*args, **kwargs)
File "C:\Users\pil562\AppData\Local\Programs\Python\Python36\lib\idlelib\run.py", line 460, in runcode
exec(code, self.locals)
File "D:\Python\DL_DG\analysis\debug\debug_01_ppi.py", line 42, in <module>
learning_rate_rbm=[0.001,0.01],rbm_gauss_visible=True)
File "D:/Python/DL_DG/Model\dbn_test.py", line 52, in __init__
sample_gauss_visible=self.sample_gauss_visible, sigma=self.sigma))
File "D:/Python/DL_DG/Model\rbm_test.py", line 358, in __init__
xavier_const,err_function,use_tqdm,tqdm)
File "D:/Python/DL_DG/Model\rbm_test.py", line 46, in __init__
self.x = tf.placeholder(tf.float32, [None, self.n_visible],name='x')
File "C:\Users\pil562\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1548, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "C:\Users\pil562\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 2094, in _placeholder
name=name)
File "C:\Users\pil562\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 767, in apply_op
op_def=op_def)
File "C:\Users\pil562\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 2630, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Users\pil562\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1204, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input/x' with dtype float and shape [?,128]
[[Node: input/x = Placeholder[dtype=DT_FLOAT, shape=[?,128], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
The error occurs at the following function:
def partial_fit(self, batch_x, k, j):
print(batch_x.dtype, batch_x.shape)
summary, _ = self.sess.run([self.merged, self.update_weights + self.update_deltas],
feed_dict={self.x: batch_x})
self.train_writer.add_summary(summary, k*self.batch_size+j)
I output the type and shape of batch_x. The shape is the same during the whole training process. The type is float64 when training the first rbm, and float32 when training the second rbm. That's where it stopped and throw out the error.
The DBN worked well when I didn't compute the summary and just used the following code:
self.sess.run(self.update_weights + self.update_deltas,feed_dict={self.x: batch_x})
It also worked well if I only train a single RBM (with or without the summary).
The batch_x used to train the second RBM is probabilities of the hidden layer in the first RBM.
Could somebody help me solve this problem? I'm not sure if the float64 is the problem.
I guess it's hard for anyone to solve the problem only with the two pieces of code I give. lol. The full code is too long to post here.
I save the output of the first RBM and use it as input to train another RBM. It works well. Thus, I think the problem is not the type or shape of the feeded batch_x, but the structure of the DBN, or the way I collected summaries.
Hope my situation can help others with similar problems.
I'm trying to restore a model using following code:
new_saver = tf.train.import_meta_graph(model_path+'.meta')
new_saver.restore(sess, model_path)
g=tf.get_default_graph()
And for each weight or bias in original graph, I did g.get_tensrr_by_name().
But when I tried to do this on a deconv2d layer, which is something like below:
def deconv2d(self,inputs, num_outputs, kernel_shape, g,scope,strides=[1, 1]):
with tf.variable_scope(scope) as scope:
weights_initializer = g.get_tensor_by_name("prsr/conditioning/deconv/Conv2d_transpose/weights:0")
biases_initializer = g.get_tensor_by_name("prsr/conditioning/deconv/Conv2d_transpose/biases:0")
return tf.contrib.layers.convolution2d_transpose(inputs=inputs, num_outputs=num_outputs,kernel_size=kernel_shape,stride=strides, \
padding='SAME', weights_initializer=weights_initializer,biases_initializer=biases_initializer)
it failed and showed following error:
File "restore.py", line 41, in deconv2d
padding='SAME', weights_initializer=weights_initializer,biases_initializer=biases_initializer)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 177, in func_with_args
return func(*args, **current_args)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1126, in convolution2d_transpose
outputs = layer.apply(inputs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.py", line 323, in apply
return self.__call__(inputs, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.py", line 289, in __call__
self.build(input_shapes[0])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/convolutional.py", line 1043, in build
dtype=self.dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 1033, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 932, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "/usr/local/lib/python2.7/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/python2.7/dist-packages/tensorflow/python/layers/base.py", line 278, in variable_getter
variable_getter=functools.partial(getter, **kwargs))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.py", line 228, in _add_variable
trainable=trainable and self.trainable)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1327, in layer_variable_getter
return _model_variable_getter(getter, *args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1316, in _model_variable_getter
custom_getter=getter, use_resource=use_resource)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 177, in func_with_args
return func(*args, **current_args)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/variables.py", line 259, in model_variable
use_resource=use_resource)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 177, in func_with_args
return func(*args, **current_args)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/variables.py", line 214, in variable
use_resource=use_resource)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 341, in _true_getter
use_resource=use_resource)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 638, in _get_single_variable
raise ValueError("If initializer is a constant, do not specify shape.")
ValueError: If initializer is a constant, do not specify shape.
I don't know which shape does this refer to, and I don't think weights_initializer and biases_initializer are constants, they are tensors, right? By the way, I'm very sure that those two tensors, prsr/conditioning/deconv/Conv2d_transpose/weights and prsr/conditioning/deconv/Conv2d_transpose/biasess exist in the original graph, since I checked this using print_tensors_in_checkpoint_file, and I can actually see the values.
So how can I restore model which applies this tf.contrib.layers.convolution2d_transpose() layer? I searched a lot on both stackoverflow and github, but nothing worked. Any help would be appreciated.
weights_initializer and bias_initializer are not what you think they are. You probably think of those two tensors as the initial values for the weights used in the deconvolution, right? However, the initializer argument is a function not a tensor that should look something like this:
def my_initializer(shape, dtype=tf.float32, partition_info=None):
# do some computation to build up a tensor of the given shape
return that_tensor
You could then use this initializer like so:
tf.contrib.layers.convolution2d_transpose(inputs=inputs, ..., weights_initializer=my_initializer)
So, as a solution to your problem, I think the following should work:
def weights_initializer(shape, dtype=tf.float32, partition_info=None):
weights = tf.get_default_graph().get_tensor_by_name("prsr/conditioning/deconv/Conv2d_transpose/weights:0")
return weights
This feels a bit hacky in my opinion, though. Why do you want to load a graph and then use pre-trained weights in a new operation? Why are those weights not associated with this operation before already when you set up the initial model?
PS: When dealing with variables you might find tf.get_variable come in handy. If you create variables using tf.get_variable you can later retrieve those variables with tf.get_variable again, without having to call the cumbersome get_tensor_by_name. Check this for more info.
I am trying to use the tensorflow inception_v3 model for a transfer learning project.I get the following error on building the model.
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
The same error does not arise on using the same script for inception_v1 model.
The models are imported from slim.nets
Running on CPU
Tensorflow version : 0.12.1
Script
import tensorflow as tf
slim = tf.contrib.slim
import models.inception_v3 as inception_v3
print("initializing model")
inputs=tf.placeholder(tf.float32, shape=[32,299,299,3])
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
logits,endpoints = inception_v3.inception_v3(inputs, num_classes=1001, is_training=False)
trainable_vars=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
for tvars in trainable_vars:
print tvars.name
Full Error Message
Traceback (most recent call last):
File "test.py", line 8, in <module>
logits,endpoints = inception_v3.inception_v3(inputs, num_classes=1001, is_training=False)
File "/home/ashish/projects/python/fashion-language/models/inception_v3.py", line 576, in inception_v3
depth_multiplier=depth_multiplier)
File "/home/ashish/projects/python/fashion-language/models/inception_v3.py", line 181, in inception_v3_base
net = array_ops.concat([branch_0, branch_1, branch_2, branch_3], 3)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 1075, in concat
dtype=dtypes.int32).get_shape(
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 669, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_util.py", line 367, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_util.py", line 302, in _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
Found my mistake, i was importing the model from https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim/python/slim/nets whereas the updated models are at https://github.com/tensorflow/models/tree/master/slim/nets.
Still haven't understood why there are two different repositories for the same classes.Must be a valid reason.