I am trying to use the graph transform tool on a tensorflow object detection model for tensorflow 1.4.1 in Linux Ubuntu 16.04
The instruction used is:
bazel-bin/tensorflow/tools/graph_transforms/transform_graph --in_graph=/tf-optimizations/pascalvoc_gtt/frozen_inference_graph.pb --out_graph=/tf-optimizations/pascalvoc_gtt/optimized_frozen_inference_graph.pb --inputs='image_tensor' --outputs='detection_boxes,detection_scores,detection_classes,num_detections' --transforms='
add_default_attributes
strip_unused_nodes(type=float)
remove_nodes(op=CheckNumerics)
fold_constants(ignore_errors=true)
fold_batch_norms
fold_old_batch_norms
fuse_resize_pad_and_conv
fuse_pad_and_conv
fuse_resize_and_conv
strip_unused_nodes
sort_by_execution_order'
When I try to query the optimized tensorflow graph,
(boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded})
I get the following error:
InvalidArgumentError: NodeDef mentions attr 'identical_element_shapes' not in Op<name=TensorArrayV3; signature=size:int32 -> handle:resource, flow:float; attr=dtype:type; attr=element_shape:shape,default=<unknown>; attr=dynamic_size:bool,default=false; attr=clear_after_read:bool,default=true; attr=tensor_array_name:string,default=""; is_stateful=true>; NodeDef: Preprocessor/map/TensorArray = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=false, tensor_array_name="", _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/map/TensorArrayUnstack/strided_slice). (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).
[[Node: Preprocessor/map/TensorArray = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=false, tensor_array_name="", _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/map/TensorArrayUnstack/strided_slice)]]
Caused by op u'Preprocessor/map/TensorArray', defined at:
File "/usr/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python2.7/dist-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelapp.py", line 478, in start
self.io_loop.start()
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 281, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 232, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 397, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-7-982229c93a39>", line 7, in <module>
tf.import_graph_def(od_graph_def, name='')
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/importer.py", line 313, in import_graph_def
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1470, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): NodeDef mentions attr 'identical_element_shapes' not in Op<name=TensorArrayV3; signature=size:int32 -> handle:resource, flow:float; attr=dtype:type; attr=element_shape:shape,default=<unknown>; attr=dynamic_size:bool,default=false; attr=clear_after_read:bool,default=true; attr=tensor_array_name:string,default=""; is_stateful=true>; NodeDef: Preprocessor/map/TensorArray = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=false, tensor_array_name="", _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/map/TensorArrayUnstack/strided_slice). (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).
[[Node: Preprocessor/map/TensorArray = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=false, tensor_array_name="", _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/map/TensorArrayUnstack/strided_slice)]]
I do not know what is wrong, as the original frozen graph works fine.
Problem solved. Its a version issue. The model was generated in a different version than the GTT.
Related
activity = model.fit(train_gen, epochs=10, # Increase number of epochs if you have sufficient hardware
validation_data=val_gen,
verbose = 1
)
Epoch 1/10
Traceback (most recent call last):
File "C:\Users\BLRCSE~1\AppData\Local\Temp/ipykernel_15312/3305335964.py", line 1, in
activity = model.fit(train_gen, epochs=10, # Increase number of epochs if you have sufficient hardware
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 54, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
InvalidArgumentError: Graph execution error:
Detected at node 'gradient_tape/sequential_1/dense_5/MatMul/MatMul' defined at (most recent call last):
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\runpy.py", line 87, in run_code
exec(code, run_globals)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\spyder_kernels\console_main.py", line 23, in
start.main()
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\spyder_kernels\console\start.py", line 328, in main
kernel.start()
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 677, in start
self.io_loop.start()
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 199, in start
self.asyncio_loop.run_forever()
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\asyncio\base_events.py", line 596, in run_forever
self._run_once()
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\asyncio\base_events.py", line 1890, in _run_once
handle._run()
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\asyncio\events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 457, in dispatch_queue
await self.process_one()
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 446, in process_one
await dispatch(*args)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 353, in dispatch_shell
await result
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 648, in execute_request
reply_content = await reply_content
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 353, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2901, in run_cell
result = self._run_cell(
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2947, in _run_cell
return runner(coro)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\IPython\core\async_helpers.py", line 68, in pseudo_sync_runner
coro.send(None)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3172, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3364, in run_ast_nodes
if (await self.run_code(code, result, async=asy)):
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3444, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "C:\Users\BLRCSE~1\AppData\Local\Temp/ipykernel_15312/1931121224.py", line 1, in
activity = model.fit(train_gen,
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\keras\engine\training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\keras\engine\training.py", line 1021, in train_function
return step_function(self, iterator)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\keras\engine\training.py", line 1010, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\keras\engine\training.py", line 1000, in run_step
outputs = model.train_step(data)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\keras\engine\training.py", line 863, in train_step
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\keras\optimizer_v2\optimizer_v2.py", line 530, in minimize
grads_and_vars = self._compute_gradients(
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\keras\optimizer_v2\optimizer_v2.py", line 583, in _compute_gradients
grads_and_vars = self._get_gradients(tape, loss, var_list, grad_loss)
File "C:\Users\BLRCSE513-WS01\anaconda3\lib\site-packages\keras\optimizer_v2\optimizer_v2.py", line 464, in _get_gradients
grads = tape.gradient(loss, var_list, grad_loss)
Node: 'gradient_tape/sequential_1/dense_5/MatMul/MatMul'
Matrix size-incompatible: In[0]: [32,2], In[1]: [120,1]
[[{{node gradient_tape/sequential_1/dense_5/MatMul/MatMul}}]] [Op:__inference_train_function_47374]
If memory growth is enabled for a PhysicalDevice, the runtime initialization will not allocate all memory on the device. Memory growth cannot be configured on a PhysicalDevice with virtual devices configured.
For example:
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
try:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
except:
# Invalid device or cannot modify virtual devices once initialized.
pass
I trying to run on a docker created as follows:
docker run --gpus=all -it -p "8888:8888" -v "/home/miguel/ml-resnet-50/:/notebooks/" --name ml-resnet-50 tensorflow/tensorflow:1.5.0-gpu-py3 jupyter notebook --ip 0.0.0.0 --no-browser --allow-root
On a Linux PC Ubuntu 20.04 with RTX 3070 Nvidia Card the follow code:
model.fit(
x=imgs_train,
y=clss_train,
batch_size=16,
epochs=2,
verbose=1,
validation_data=(imgs_val, clss_val)
)
And getting following error:
InternalError: Blas SGEMM launch failed : m=48400, n=64, k=64
[[Node: res2a_branch2a/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC",
dilations=[1, 1, 1, 1], padding="VALID", strides=[1, 1, 1, 1],
use_cudnn_on_gpu=true,
_device="/job:localhost/replica:0/task:0/device:GPU:0"](max_pooling2d/MaxPool,
res2a_branch2a/kernel/read)]] [[Node: loss/mul/_2859 =
_Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0",
send_device="/job:localhost/replica:0/task:0/device:GPU:0",
send_device_incarnation=1, tensor_name="edge_15435_loss/mul",
tensor_type=DT_FLOAT,
_device="/job:localhost/replica:0/task:0/device:CPU:0"]]
Caused by op 'res2a_branch2a/Conv2D', defined at: File
"/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main
"main", mod_spec) File "/usr/lib/python3.5/runpy.py", line 85, in _run_code
exec(code, run_globals) File "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py", line
16, in
app.launch_new_instance() File "/usr/local/lib/python3.5/dist-packages/traitlets/config/application.py",
line 658, in launch_instance
app.start() File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelapp.py", line
478, in start
self.io_loop.start() File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/ioloop.py", line
177, in start
super(ZMQIOLoop, self).start() File "/usr/local/lib/python3.5/dist-packages/tornado/ioloop.py", line 888,
in start
handler_func(fd_obj, events) File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py",
line 277, in null_wrapper
return fn(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py",
line 440, in _handle_events
self._handle_recv() File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py",
line 472, in _handle_recv
self._run_callback(callback, msg) File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py",
line 414, in _run_callback
callback(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py",
line 277, in null_wrapper
return fn(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line
283, in dispatcher
return self.dispatch_shell(stream, msg) File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line
233, in dispatch_shell
handler(stream, idents, msg) File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line
399, in execute_request
user_expressions, allow_stdin) File "/usr/local/lib/python3.5/dist-packages/ipykernel/ipkernel.py", line
208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent) File
"/usr/local/lib/python3.5/dist-packages/ipykernel/zmqshell.py", line
537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File
"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py",
line 2728, in run_cell
interactivity=interactivity, compiler=compiler, result=result) File
"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py",
line 2850, in run_ast_nodes
if self.run_code(code, result): File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py",
line 2910, in run_code
exec(code_obj, self.user_global_ns, self.user_ns) File "", line 2, in
model = get_model() File "", line 4, in get_model
model = ResNet50(include_top=False,input_shape=(pipeline['img_height'],
pipeline['img_width'], 3)) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/_impl/keras/applications/resnet50.py",
line 235, in ResNet50
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/_impl/keras/applications/resnet50.py",
line 122, in conv_block
name=conv_name_base + '2a')(input_tensor) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/_impl/keras/engine/topology.py",
line 258, in call
output = super(Layer, self).call(inputs, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py",
line 652, in call
outputs = self.call(inputs, *args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/convolutional.py",
line 167, in call
outputs = self._convolution_op(inputs, self.kernel) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py",
line 838, in call
return self.conv_op(inp, filter) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py",
line 502, in call
return self.call(inp, filter) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py",
line 190, in call
name=self.name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_nn_ops.py",
line 639, in conv2d
data_format=data_format, dilations=dilations, name=name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py",
line 787, in _apply_op_helper
op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py",
line 3160, in create_op
op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py",
line 1625, in init
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InternalError (see above for traceback): Blas SGEMM launch failed :
m=48400, n=64, k=64 [[Node: res2a_branch2a/Conv2D =
Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1],
padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true,
_device="/job:localhost/replica:0/task:0/device:GPU:0"](max_pooling2d/MaxPool,
res2a_branch2a/kernel/read)]] [[Node: loss/mul/_2859 =
_Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0",
send_device="/job:localhost/replica:0/task:0/device:GPU:0",
send_device_incarnation=1, tensor_name="edge_15435_loss/mul",
tensor_type=DT_FLOAT,
_device="/job:localhost/replica:0/task:0/device:CPU:0"]]
Any idea why it happeds?
I got error, trying to chatbot example in tensorflow( https://github.com/golbin/TensorFlow-Tutorials/tree/master/08%20-%20RNN/ChatBot)
I have no idea what error is.
bellow is error
Caused by op 'save/RestoreV2_11', defined at:
File "chat.py", line 79, in <module>
tf.app.run()
File "/home/ubuntu/tensor/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "chat.py", line 75, in main
chatbot = ChatBot(FLAGS.voc_path, FLAGS.train_dir)
File "chat.py", line 17, in __init__
self.model = Seq2Seq(self.dialog.vocab_size)
File "/home/ubuntu/example/TensorFlow-Tutorials/08 - RNN/ChatBot/model.py", line 30, in __init_ _
self.saver = tf.train.Saver(tf.global_variables())
File "/home/ubuntu/tensor/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 1140, in __init__
self.build()
File "/home/ubuntu/tensor/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 1172, in build
filename=self._filename)
File "/home/ubuntu/tensor/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 688, in build
restore_sequentially, reshape)
File "/home/ubuntu/tensor/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 407, in _AddRestoreOps
tensors = self.restore_op(filename_tensor, saveable, preferred_shard)
File "/home/ubuntu/tensor/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 247, in restore_op
[spec.tensor.dtype])[0])
File "/home/ubuntu/tensor/lib/python3.5/site-packages/tensorflow/python/ops/gen_io_ops.py", line 663, in restore_v2
dtypes=dtypes, name=name)
File "/home/ubuntu/tensor/lib/python3.5/site-packages/tensorflow/python/framework/op_def_librar y.py", line 767, in apply_op
op_def=op_def)
File "/home/ubuntu/tensor/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2630, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/ubuntu/tensor/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1204, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
NotFoundError (see above for traceback): Key
decode/rnn/multi_rnn_cell/cell_1/basic_rnn_cell/bias
not found in checkpoint
[[Node: save/RestoreV2_11 = RestoreV2[dtypes=[DT_FLOAT],
_device="/job:localhost/replica
:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_11/tensor_names,
save/RestoreV2_11/shape_and_slices)]]
You need to apply this solution
by using the appropriate vars_to_rename
likely using this
"lstm/BasicLSTMCell/Linear/Bias": "lstm/basic_lstm_cell/biases"
I have implemented autoencoder on my custom images data for sign language recognition.Now i want to save tensors object of the output layer to an numpy array. I tried Session.run(tensor) and tensor.eval(). Here is my code.
#define model
x= tf.placeholder(tf.float32,[None,784])
y_=tf.placeholder(tf.float32,[None,6])
k=190
l=180
m=150
n=130
o=100
num_of_epoch=10
w1=tf.Variable(tf.truncated_normal([784,k],stddev=0.1))
b1=tf.Variable(tf.zeros([k]))
w2=tf.Variable(tf.truncated_normal([k,l],stddev=0.1))
b2=tf.Variable(tf.zeros([l]))
w3=tf.Variable(tf.truncated_normal([l,m],stddev=0.1))
b3=tf.Variable(tf.zeros([m]))
w4=tf.Variable(tf.truncated_normal([m,n],stddev=0.1))
b4=tf.Variable(tf.zeros([n]))
w5=tf.Variable(tf.truncated_normal([n,o],stddev=0.1))
b5=tf.Variable(tf.zeros([o]))
w6=tf.Variable(tf.truncated_normal([o,6],stddev=0.1))
b6=tf.Variable(tf.zeros([6]))
y1=tf.nn.relu(tf.matmul(x,w1)+b1)
y2=tf.nn.relu(tf.matmul(y1,w2)+b2)
y3=tf.nn.relu(tf.matmul(y2,w3)+b3)
y4=tf.nn.relu(tf.matmul(y3,w4)+b4)
y5=tf.nn.relu(tf.matmul(y4,w5)+b5)
y=tf.nn.softmax(tf.matmul(y5,w6)+b6)
cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),
reduction_indices=[1]))
train_step=tf.train.GradientDescentOptimizer(0.03).minimize(cross_entropy)
init=tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for i in range(num_of_epoch):
train_data = {x:x_train,y_:y_train}
sess.run(train_step,feed_dict=train_data)
currect_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(currect_prediction,tf.float32))
sess.run(accuracy,feed_dict={x:x_train,y_:y_train})
currect_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(currect_prediction,tf.float32))
sess.run(accuracy,feed_dict= {x:x_test,y_:y_test})
y_p = tf.argmax(y, 1).eval() #this line shows me the error
print(y_p)
I am getting the below error. How can I fix this error and save tensor data to numpy array ?
Traceback (most recent call last):
File "<ipython-input-45-5e38490a3e8e>", line 1, in <module>
runfile('C:/Users/RIFAT/PycharmProjects/tensorflow_autoencoder
/autoencoderreconstruction.py',
wdir='C:/Users/RIFAT/PycharmProjects/tensorflow_autoencoder')
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils
\site\sitecustomize.py", line 880, in runfile
execfile(filename, namespace)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils
\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/RIFAT/PycharmProjects/tensorflow_autoencoder
/autoencoderreconstruction.py", line 112, in <module>
y_p = tf.argmax(y, 1).eval()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\framework\ops.py", line 606, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\framework\ops.py", line 3928, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python\client
\session.py", line 789, in run
run_metadata_ptr)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python\client
\session.py", line 997, in _run
feed_dict_string, options, run_metadata)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python\client
\session.py", line 1132, in _do_run
target_list, options, run_metadata)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python\client
\session.py", line 1152, in _do_call
raise type(e)(node_def, op, message)
InvalidArgumentError: Shape [-1,784] has negative dimensions
[[Node: Placeholder_62 = Placeholder[dtype=DT_FLOAT, shape=[?,784],
_device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'Placeholder_62', defined at:
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils\ipython
\start_kernel.py", line 231, in <module>
main()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils\ipython
\start_kernel.py", line 227, in main
kernel.start()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\ipykernel\kernelapp.py",
line 477, in start
ioloop.IOLoop.instance().start()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\zmq\eventloop
\ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tornado\ioloop.py", line
888, in start
handler_func(fd_obj, events)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tornad
\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\zmq\eventloop
\zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\zmq\eventloop
\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\zmq\eventloop
\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tornado
\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\ipykernel
\kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\ipykernel
\kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\ipykernel
\kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\ipykernel\ipkernel.py",
line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\ipykernel\zmqshell.py",
line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\IPython
\core\interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\IPython
\core\interactiveshell.py", line 2827, in run_ast_nodes
if self.run_code(code, result):
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\IPython
\core\interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-45-5e38490a3e8e>", line 1, in <module>
runfile('C:/Users/RIFAT/PycharmProjects/tensorflow_autoencoder
/autoencoderreconstruction.py', wdir='C:/Users/RIFAT/PycharmProjects
/tensorflow_autoencoder')
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils
\site\sitecustomize.py", line 880, in runfile
execfile(filename, namespace)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\spyder\utils
\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/RIFAT/PycharmProjects/tensorflow_autoencoder
/autoencoderreconstruction.py", line 62, in <module>
x= tf.placeholder(tf.float32,[None,784])
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\ops\array_ops.py", line 1530, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\ops\gen_array_ops.py", line 1954, in _placeholder
name=name)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\framework\op_def_library.py", line 767, in apply_op
op_def=op_def)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\framework\ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Users\RIFAT\Anaconda3\lib\site-packages\tensorflow\python
\framework\ops.py", line 1269, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Shape [-1,784] has
negative dimensions
[[Node: Placeholder_62 = Placeholder[dtype=DT_FLOAT, shape=[?,784],
_device="/job:localhost/replica:0/task:0/cpu:0"]()]]
That's because y is a tensor in the graph and not a variable. When you run .eval() on a variable, it gives you the current value held by that variable in that session but if you run .eval() on a tensor instead like tf.argmax(y, 1).eval() in your case then tensor flow runs the graph to that node to get the value of that node. And since in your case, it doesn't get the value of the placeholder x and y_ while running the graph, it gives the error. One way to resolve this error is by passing the values of the placeholders in your eval call like this:
tf.argmax(y, 1).eval(feed_dict= {x:x_test,y_:y_test})
But, a more preferred way is giving the context of your session to the eval call in which case it will return the value of the tensor. Ex:
tf.argmax(y, 1).eval(session = sess)
Your question is not 100% clear. But the error you are seeing is caused by the fact that you try to run the graph without the feed dict in place. To see the output of the prediction (that is with the argmax(y, 1) present) you'd simply run:
y_p = sess.run(tf.argmax(y, 1), feed_dict=train_data)
print(y_p)
But that will give you the actual predicted value (on the train data as this is fed, to get this on the test data, simply geed in the test_data). To get the probabilities you'd pull y without the argmax:
y_p = sess.run(y, feed_dict=train_data)
print(y_p)
I got confused about feed_dict. I can feed my datasets as X but I cannot feed Y labels. all dtype are tf.float64 and I printed it out, and yes, they are float64. My X dataset has shape 90,32,32,3 and my Y is 90,2. so I defined a plaeholder to be fed with the same shape.
but when I run my code, error comes out :
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_34' with dtype double
[[Node: Placeholder_34 = Placeholder[dtype=DT_DOUBLE, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
I have no idea why this happened, since I am really sure that both X and Y are float64. X is fine but why only Y? Please help, I really appreciate :)
here's the code :
size_image =32
allX = np.zeros((100, size_image, size_image, 3), dtype='float64')
ally = np.zeros(100, dtype='float64')
count = 0
for i in range(49):
img = io.imread("cat/cat"+str(i+1)+".jpg")
new_img = imresize(img, (size_image, size_image, 3))
allX[count] = np.array(new_img)
ally[count] = 0
count += 1
#dog images and labels
for i in range(51):
img = io.imread("dog/dog"+str(i+1)+".jpg")
new_img = imresize(img, (size_image, size_image, 3))
allX[count] = np.array(new_img)
ally[count] = 1
count += 1
#splitting dataset supaya random
X, X_test, Y, Y_test = train_test_split(allX, ally, test_size=0.1, random_state=42)
# encode the Ys
Y = to_categorical(Y, 2)
Y_test = to_categorical(Y_test, 2)
x = tf.placeholder(tf.float64, [None,None, None,3])
y_ = tf.placeholder(tf.float64, [None, 2])
sess = tf.Session()
sess.run(init)
t = x*2
print(sess.run(t, feed_dict={x:X}))
m = 1*y
print(sess.run(m, feed_dict={y_:Y}))
and here is the traceback :
InvalidArgumentError Traceback (most recent call last)
<ipython-input-95-6f97fe1da9c0> in <module>()
6 print(y.get_shape())
7 m = 1*y
----> 8 print(sess.run(m, feed_dict={y_:Y}))
9 print(y_.get_shape().as_list())
10 #x = tf.stack(X)
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_34' with dtype double
[[Node: Placeholder_34 = Placeholder[dtype=DT_DOUBLE, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op u'Placeholder_34', defined at:
File "/usr/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py", line 3, in <module>
app.launch_new_instance()
File "/usr/local/lib/python2.7/dist-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelapp.py", line 474, in start
ioloop.IOLoop.instance().start()
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py", line 887, in start
handler_func(fd_obj, events)
File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 276, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 390, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python2.7/dist-packages/ipykernel/zmqshell.py", line 501, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2821, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-94-297273a8e189>", line 1, in <module>
x = tf.placeholder(tf.float64, [None,None, None,3])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 1520, in placeholder
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 2149, in _placeholder
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2395, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1264, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_34' with dtype double
[[Node: Placeholder_34 = Placeholder[dtype=DT_DOUBLE, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]