I'm trying to make a neural network using keras and everytime I try to add a layer I get a list of errors relating to the call the way I'm calling it is model.add(Dense(768,input_dim=3072,init='uniform',activation='relu'))
and the errors I get are the following:
Traceback (most recent call last):
File "nn2.py", line 52, in <module>
model.add(Dense(768,input_dim=3072,init='uniform',activation='relu'))
File "/Users/lens/Documents/NNproject/neuralenv/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py", line 1132, in __init__
activity_regularizer=regularizers.get(activity_regularizer), **kwargs)
File "/Users/lens/Documents/NNproject/neuralenv/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py", line 456, in _method_wrapper
result = method(self, *args, **kwargs)
File "/Users/lens/Documents/NNproject/neuralenv/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 294, in __init__
generic_utils.validate_kwargs(kwargs, allowed_kwargs)
File "/Users/lens/Documents/NNproject/neuralenv/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 792, in validate_kwargs
raise TypeError(error_message, kwarg)
TypeError: ('Keyword argument not understood:', 'init')
Does anyone have a fix for this?
There isn't a single init argument for keras Dense layers. You'll need to specify the initialization for kernel_initializer and bias_initializer separately.
Related
I am using Faster RCNN, repo that I am using can be found in the link, to detect cars in a video frame. I used Keras 2.2.3 and Tensorflow 1.15.0. I want to deploy and run it on my Android device. Each part in Faster RCNN is implemented in Keras and in order to deploy it on Android I want to convert them to TF Lite model. The final network, the classifier, has a custom layer which is called RoiPoolingConv and I cannot convert the final network to TF Lite. At first, I have tried the following
converter = tf.lite.TFLiteConverter.from_keras_model_file('model_classifier_with_architecture.h5',
custom_objects={"RoiPoolingConv": RoiPoolingConv})
tfmodel = converter.convert()
open ("model_cls.tflite" , "wb") .write(tfmodel)
This gives the following error
Traceback (most recent call last):
File "Keras-FasterRCNN/model_to_tflite.py", line 26, in <module>
custom_objects={"RoiPoolingConv": RoiPoolingConv})
File "/home/alp/.local/lib/python3.6/site-packages/tensorflow/lite/python/lite.py", line 747, in from_keras_model_file
keras_model = _keras.models.load_model(model_file, custom_objects)
File "/home/alp/.local/lib/python3.6/site-packages/tensorflow/python/keras/saving/save.py", line 146, in load_model
return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
File "/home/alp/.local/lib/python3.6/site-packages/tensorflow/python/keras/saving/hdf5_format.py", line 212, in load_model_from_hdf5
custom_objects=custom_objects)
File "/home/alp/.local/lib/python3.6/site-packages/tensorflow/python/keras/saving/model_config.py", line 55, in model_from_config
return deserialize(config, custom_objects=custom_objects)
File "/home/alp/.local/lib/python3.6/site-packages/tensorflow/python/keras/layers/serialization.py", line 89, in deserialize
printable_module_name='layer')
File "/home/alp/.local/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 192, in deserialize_keras_object
list(custom_objects.items())))
File "/home/alp/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1131, in from_config
process_node(layer, node_data)
File "/home/alp/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1089, in process_node
layer(input_tensors, **kwargs)
File "/home/alp/.local/lib/python3.6/site-packages/keras/engine/base_layer.py", line 475, in __call__
previous_mask = _collect_previous_mask(inputs)
File "/home/alp/.local/lib/python3.6/site-packages/keras/engine/base_layer.py", line 1441, in _collect_previous_mask
mask = node.output_masks[tensor_index]
AttributeError: 'Node' object has no attribute 'output_masks'
As a workaround I tried was to convert Keras models to Tensorflow frozen graph and then do the TF Lite conversion on these frozen graphs. This yields the following error
Traceback (most recent call last):
File "/home/alp/.local/bin/toco_from_protos", line 11, in <module>
sys.exit(main())
File "/home/alp/.local/lib/python3.6/site-packages/tensorflow/lite/toco/python/toco_from_protos.py", line 59, in main
app.run(main=execute, argv=[sys.argv[0]] + unparsed)
File "/home/alp/.local/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "/home/alp/.local/lib/python3.6/site-packages/absl/app.py", line 299, in run
_run_main(main, args)
File "/home/alp/.local/lib/python3.6/site-packages/absl/app.py", line 250, in _run_main
sys.exit(main(argv))
File "/home/alp/.local/lib/python3.6/site-packages/tensorflow/lite/toco/python/toco_from_protos.py", line 33, in execute
output_str = tensorflow_wrap_toco.TocoConvert(model_str, toco_str, input_str)
Exception: We are continually in the process of adding support to TensorFlow Lite for more ops. It would be helpful if you could inform us of how this conversion went by opening a github issue at https://github.com/tensorflow/tensorflow/issues/new?template=40-tflite-op-request.md
and pasting the following:
Some of the operators in the model are not supported by the standard TensorFlow Lite runtime. If those are native TensorFlow operators, you might be able to use the extended runtime by passing --enable_select_tf_ops, or by setting target_ops=TFLITE_BUILTINS,SELECT_TF_OPS when calling tf.lite.TFLiteConverter(). Otherwise, if you have a custom implementation for them you can disable this error with --allow_custom_ops, or by setting allow_custom_ops=True when calling tf.lite.TFLiteConverter(). Here is a list of builtin operators you are using: ADD, CAST, CONCATENATION, CONV_2D, DEPTHWISE_CONV_2D, FULLY_CONNECTED, MUL, PACK, RESHAPE, RESIZE_BILINEAR, SOFTMAX, STRIDED_SLICE. Here is a list of operators for which you will need custom implementations: AddV2.
Is there a way to achieve the conversion of model with custom layer to TF Lite model?
I am running the Generative Adversarial Network in my personal system and I am getting the error provided as below, it may be because of GPU accessing problem as explained in this link: (Function call stack: keras_scratch_graph Error)
Since I want to run the code in my personal system which does not consist with GPU then how to manage that the code should not access the GPU?
The python code is provided in this link: (https://github.com/eriklindernoren/Keras-GAN/tree/master/pix2pix), where the running code is present in pix2pix.py file.
Produced Error is as follow:
C:\ProgramData\Anaconda2\envs\GaitRecognitionCNN-master13\lib\site-packages\keras\engine\training.py:297: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?
'Discrepancy between trainable weights and collected trainable'
2020-04-08 17:42:33.366720: W tensorflow/core/common_runtime/base_collective_executor.cc:217] BaseCollectiveExecutor::StartAbort Failed precondition: Error while reading resource variable _AnonymousVar131 from Container: localhost. This could mean that the variable was uninitialized. Not found: Resource localhost/_AnonymousVar131/class tensorflow::Var does not exist.
[[{{node mul_33/ReadVariableOp}}]]
Traceback (most recent call last):
File "H:/data_rar_and_others/Code_For_GAN5/pix2pix.py", line 217, in <module>
gan.train(epochs=220, batch_size=4, sample_interval=50)
File "H:/data_rar_and_others/Code_For_GAN5/pix2pix.py", line 165, in train
d_loss_real = self.discriminator.train_on_batch([imgs_A, imgs_B], valid)
File "C:\ProgramData\Anaconda2\envs\GaitRecognitionCNN-master13\lib\site-packages\keras\engine\training.py", line 1514, in train_on_batch
outputs = self.train_function(ins)
File "C:\ProgramData\Anaconda2\envs\GaitRecognitionCNN-master13\lib\site-packages\tensorflow_core\python\keras\backend.py", line 3727, in __call__
outputs = self._graph_fn(*converted_inputs)
File "C:\ProgramData\Anaconda2\envs\GaitRecognitionCNN-master13\lib\site-packages\tensorflow_core\python\eager\function.py", line 1551, in __call__
return self._call_impl(args, kwargs)
File "C:\ProgramData\Anaconda2\envs\GaitRecognitionCNN-master13\lib\site-packages\tensorflow_core\python\eager\function.py", line 1591, in _call_impl
return self._call_flat(args, self.captured_inputs, cancellation_manager)
File "C:\ProgramData\Anaconda2\envs\GaitRecognitionCNN-master13\lib\site-packages\tensorflow_core\python\eager\function.py", line 1692, in _call_flat
ctx, args, cancellation_manager=cancellation_manager))
File "C:\ProgramData\Anaconda2\envs\GaitRecognitionCNN-master13\lib\site-packages\tensorflow_core\python\eager\function.py", line 545, in call
ctx=ctx)
File "C:\ProgramData\Anaconda2\envs\GaitRecognitionCNN-master13\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.FailedPreconditionError: Error while reading resource variable _AnonymousVar131 from Container: localhost. This could mean that the variable was uninitialized. Not found: Resource localhost/_AnonymousVar131/class tensorflow::Var does not exist.
[[node mul_33/ReadVariableOp (defined at C:\ProgramData\Anaconda2\envs\GaitRecognitionCNN-master13\lib\site-packages\keras\backend\tensorflow_backend.py:3009) ]] [Op:__inference_keras_scratch_graph_6898]
Function call stack:
keras_scratch_graph
This error has been resolved when I have changed the
from keras.optimizers import Adam
with the following
from tensorflow.keras.optimizers import Adam
I am really new to torch and machine learning. I am trying to run my project using GPU. I tried to have such modification to my code:
model = Challenge()
model = model.to(torch.device('cuda'))
However, I am still having following error:
Traceback (most recent call last):
File "C:/Users/ruidong/Desktop/YZR temp/Project2/train_challenge.py", line 112, in <module>
main()
File "C:/Users/ruidong/Desktop/YZR temp/Project2/train_challenge.py", line 91, in main
stats)
File "C:/Users/ruidong/Desktop/YZR temp/Project2/train_challenge.py", line 40, in _evaluate_epoch
output = model(X)
File "C:\Users\ruidong\Anaconda3\envs\EECS445\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\ruidong\Desktop\YZR temp\Project2\model\challenge.py", line 48, in forward
z = F.relu(self.conv1(x))
File "C:\Users\ruidong\Anaconda3\envs\EECS445\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\ruidong\Anaconda3\envs\EECS445\lib\site-packages\torch\nn\modules\conv.py", line 345, in forward
return self.conv2d_forward(input, self.weight)
File "C:\Users\ruidong\Anaconda3\envs\EECS445\lib\site-packages\torch\nn\modules\conv.py", line 342, in conv2d_forward
self.padding, self.dilation, self.groups)
RuntimeError: Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _thnn_conv2d_forward
Any suggestions? really appreciate.
The model is correctly moved to GPU. However, for a model that is placed in GPU, you need to pass the tensors that are in GPU too. The error is because you are passing tensor that is placed in CPU in a model that is in GPU. Just do the same for inputs before passing them to model
I had a Tensorflow code that worked well with python 2. I need to switch to Python 3. besides, minor changes, this is the big error I am getting. Something is causing input feeding error.
Traceback (most recent call last):
File "main.py", line 530, in <module>
train(config)
File "main.py", line 337, in train
loss,train_op = trainer.step(sess,batch)
File "trainer.py", line 33, in step
loss, train_op = sess.run([self.loss,self.train_op],feed_dict=feed_dict)
File "anaconda2/envs/tf_gpu/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 905, in run
run_metadata_ptr)
File "anaconda2/envs/tf_gpu/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1116, in _run
str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (0,) for Tensor 'simple_lstm/pre_emb_mat:0', which has shape '(?, 100)'
How could I resolve it?
I am trying to modify an inference code for pruned SqueezeNet network
However, I faced the following error. Could anyone comment how to go around this cpu/gpu backend error ?
[kevin#linux SqueezeNet-Pruning]$ python predict.py --image “3_100.jpg” --model “model_prunned” --num_class “2”
prediction in progress
Traceback (most recent call last):
File “predict.py”, line 63, in
prediction = predict_image(imagepath)
File “predict.py”, line 47, in predict_image
output = model(input)
File “/usr/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 477, in call
result = self.forward(*input, **kwargs)
File “/home/kevin/Documents/Grive/Personal/Coursera/Machine_Learning/pruning/Pruning-CNN/SqueezeNet-Pruning/finetune.py”, line 39, in forward
x = self.features(x)
File “/usr/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 477, in call
result = self.forward(*input, **kwargs)
File “/usr/lib/python3.7/site-packages/torch/nn/modules/container.py”, line 92, in forward
input = module(input)
File “/usr/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 477, in call
result = self.forward(*input, **kwargs)
File “/usr/lib/python3.7/site-packages/torch/nn/modules/conv.py”, line 313, in forward
self.padding, self.dilation, self.groups)
RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 ‘weight’
[kevin#linux SqueezeNet-Pruning]$
I think it can relate to the use of GPU. I think the program might work with correct configuration with GPU. Or you can delete line 39, 40.