I'm going over the Book Deep Learning with Python from F. Chollet.
https://www.manning.com/books/deep-learning-with-python
I'm trying to follow along with the code examples. I just installed keras, and I am getting this error when trying to run this:
from this notebook:
https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/2.1-a-first-look-at-a-neural-network.ipynb
from keras import models
from keras import layers
network = models.Sequential()
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softmax'))
TypeError Traceback (most recent call
last) in ()
4 network = models.Sequential()
5 network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
----> 6 network.add(layers.Dense(10, activation='softmax'))
~/anaconda3/lib/python3.6/site-packages/keras/engine/sequential.py in
add(self, layer)
179 self.inputs = network.get_source_inputs(self.outputs[0])
180 elif self.outputs:
--> 181 output_tensor = layer(self.outputs[0])
182 if isinstance(output_tensor, list):
183 raise TypeError('All layers in a Sequential model '
~/anaconda3/lib/python3.6/site-packages/keras/engine/base_layer.py in
call(self, inputs, **kwargs)
455 # Actually call the layer,
456 # collecting output(s), mask(s), and shape(s).
--> 457 output = self.call(inputs, **kwargs)
458 output_mask = self.compute_mask(inputs, previous_mask)
459
~/anaconda3/lib/python3.6/site-packages/keras/layers/core.py in
call(self, inputs)
881 output = K.bias_add(output, self.bias, data_format='channels_last')
882 if self.activation is not None:
--> 883 output = self.activation(output)
884 return output
885
~/anaconda3/lib/python3.6/site-packages/keras/activations.py in
softmax(x, axis)
29 raise ValueError('Cannot apply softmax to a tensor that is 1D')
30 elif ndim == 2:
---> 31 return K.softmax(x)
32 elif ndim > 2:
33 e = K.exp(x - K.max(x, axis=axis, keepdims=True))
~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py
in softmax(x, axis) 3229 A tensor. 3230 """
-> 3231 return tf.nn.softmax(x, axis=axis) 3232 3233
TypeError: softmax() got an unexpected keyword argument 'axis'
I'm wondering if there's something off with my installation?
keras.__version__
2.2.4
If anyone could give me a clue of what to look into.
Seems like you have an incompatible Tensorflow version (which Keras is using as a backend). For details look here
Related
Question
GridSearchCV via KerasClassifier causes the error when Keras Normalization has been adapted to data. Without the adapted Normalization, it works. The reason why using Normalization is because it gave better result than simply divide by 255.0.
PicklingError: Could not pickle the task to send it to the workers.
Workaround
By setting n_jobs=1 not to multi-thread, it works but perhaps not much use to run single thread.
Environment
Python 3.9.13
TensorFlow version: 2.10.0
Eager execution is: True
Keras version: 2.10.0
sklearn version: 1.1.3
Code
import numpy as np
import tensorflow as tf
from keras.layers import (
Dense,
Flatten,
Normalization,
Conv2D,
MaxPooling2D,
)
from keras.models import (
Sequential
)
from scikeras.wrappers import (
KerasClassifier,
)
from sklearn.model_selection import (
GridSearchCV
)
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
# max_value = float(np.max(x_train))
# x_train, x_test = x_train/max_value, x_test/max_value
input_shape = x_train[0].shape
number_of_classes = 10
# Data Normalization
normalization = Normalization(
name="norm",
input_shape=input_shape, # (32, 32, 3)
axis=-1 # Regard each pixel as a feature
)
normalization.adapt(x_train)
def create_model():
model = Sequential([
# Without the adapted Normalization layer, it works.
normalization,
Conv2D(
name="conv",
filters=32,
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
activation='relu',
input_shape=input_shape
),
MaxPooling2D(
name="maxpool",
pool_size=(2, 2)
),
Flatten(),
Dense(
name="full",
units=100,
activation="relu"
),
Dense(
name="label",
units=number_of_classes,
activation="softmax"
)
])
model.compile(
loss=tf.keras.losses.sparse_categorical_crossentropy,
optimizer='adam',
metrics=['accuracy']
)
return model
model = KerasClassifier(model=create_model, verbose=2)
batch_size = [32]
epochs = [2, 3]
param_grid = dict(batch_size=batch_size, epochs=epochs)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)
grid_result = grid.fit(x_train, y_train)
Log
The above exception was the direct cause of the following exception:
PicklingError Traceback (most recent call last)
Cell In [28], line 7
4 param_grid = dict(batch_size=batch_size, epochs=epochs)
6 grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)
----> 7 grid_result = grid.fit(x_train, y_train)
File ~/venv/ml/lib/python3.9/site-packages/sklearn/model_selection/_search.py:875, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
869 results = self._format_results(
870 all_candidate_params, n_splits, all_out, all_more_results
871 )
873 return results
--> 875 self._run_search(evaluate_candidates)
877 # multimetric is determined here because in the case of a callable
878 # self.scoring the return type is only known after calling
879 first_test_score = all_out[0]["test_scores"]
File ~/venv/ml/lib/python3.9/site-packages/sklearn/model_selection/_search.py:1379, in GridSearchCV._run_search(self, evaluate_candidates)
1377 def _run_search(self, evaluate_candidates):
1378 """Search all candidates in param_grid"""
-> 1379 evaluate_candidates(ParameterGrid(self.param_grid))
File ~/venv/ml/lib/python3.9/site-packages/sklearn/model_selection/_search.py:822, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
814 if self.verbose > 0:
815 print(
816 "Fitting {0} folds for each of {1} candidates,"
817 " totalling {2} fits".format(
818 n_splits, n_candidates, n_candidates * n_splits
819 )
820 )
--> 822 out = parallel(
823 delayed(_fit_and_score)(
824 clone(base_estimator),
825 X,
826 y,
827 train=train,
828 test=test,
829 parameters=parameters,
830 split_progress=(split_idx, n_splits),
831 candidate_progress=(cand_idx, n_candidates),
832 **fit_and_score_kwargs,
833 )
834 for (cand_idx, parameters), (split_idx, (train, test)) in product(
835 enumerate(candidate_params), enumerate(cv.split(X, y, groups))
836 )
837 )
839 if len(out) < 1:
840 raise ValueError(
841 "No fits were performed. "
842 "Was the CV iterator empty? "
843 "Were there no candidates?"
844 )
File ~/venv/ml/lib/python3.9/site-packages/joblib/parallel.py:1098, in Parallel.__call__(self, iterable)
1095 self._iterating = False
1097 with self._backend.retrieval_context():
-> 1098 self.retrieve()
1099 # Make sure that we get a last message telling us we are done
1100 elapsed_time = time.time() - self._start_time
File ~/venv/ml/lib/python3.9/site-packages/joblib/parallel.py:975, in Parallel.retrieve(self)
973 try:
974 if getattr(self._backend, 'supports_timeout', False):
--> 975 self._output.extend(job.get(timeout=self.timeout))
976 else:
977 self._output.extend(job.get())
File ~/venv/ml/lib/python3.9/site-packages/joblib/_parallel_backends.py:567, in LokyBackend.wrap_future_result(future, timeout)
564 """Wrapper for Future.result to implement the same behaviour as
565 AsyncResults.get from multiprocessing."""
566 try:
--> 567 return future.result(timeout=timeout)
568 except CfTimeoutError as e:
569 raise TimeoutError from e
File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py:446, in Future.result(self, timeout)
444 raise CancelledError()
445 elif self._state == FINISHED:
--> 446 return self.__get_result()
447 else:
448 raise TimeoutError()
File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py:391, in Future.__get_result(self)
389 if self._exception:
390 try:
--> 391 raise self._exception
392 finally:
393 # Break a reference cycle with the exception in self._exception
394 self = None
PicklingError: Could not pickle the task to send it to the workers.
Research
Keras KerasClassifier gridsearch TypeError: can't pickle _thread.lock objects told Keras did not support pickle was the cause. However, as the code works if the adapted Normalization is not used, not relevant.
GPU can cause the issue but there is no GPU in my environment.
References
SciKeras Basic usage¶
How to Grid Search Hyperparameters for Deep Learning Models in Python with Keras
mixed_precision.set_global_policy(policy="mixed_float16") gives an error when I add this line
error =
TypeError Traceback (most recent call
last) in
5 #mixed_precision.set_global_policy(policy="float32")
6 input_shape = (224, 224, 3)
----> 7 base_model = tf.keras.applications.EfficientNetB0(include_top=False)
8 base_model.trainable = False # freeze base model layers
9
4 frames
/usr/local/lib/python3.7/dist-packages/keras/applications/efficientnet.py
in EfficientNetB0(include_top, weights, input_tensor, input_shape,
pooling, classes, classifier_activation, **kwargs)
559 classes=classes,
560 classifier_activation=classifier_activation,
--> 561 **kwargs)
562
563
/usr/local/lib/python3.7/dist-packages/keras/applications/efficientnet.py
in EfficientNet(width_coefficient, depth_coefficient, default_size,
dropout_rate, drop_connect_rate, depth_divisor, activation,
blocks_args, model_name, include_top, weights, input_tensor,
input_shape, pooling, classes, classifier_activation)
332 # original implementation.
333 # See https://github.com/tensorflow/tensorflow/issues/49930 for more details
--> 334 x = x / tf.math.sqrt(IMAGENET_STDDEV_RGB)
335
336 x = layers.ZeroPadding2D(
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py
in error_handler(*args, **kwargs)
151 except Exception as e:
152 filtered_tb = _process_traceback_frames(e.traceback)
--> 153 raise e.with_traceback(filtered_tb) from None
154 finally:
155 del filtered_tb
/usr/local/lib/python3.7/dist-packages/keras/layers/core/tf_op_layer.py
in handle(self, op, args, kwargs)
105 isinstance(x, keras_tensor.KerasTensor)
106 for x in tf.nest.flatten([args, kwargs])):
--> 107 return TFOpLambda(op)(*args, **kwargs)
108 else:
109 return self.NOT_SUPPORTED
/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py
in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.traceback)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
TypeError: Exception encountered when calling layer
"tf.math.truediv_3" (type TFOpLambda).
x and y must have the same dtype, got tf.float16 != tf.float32.
Call arguments received by layer "tf.math.truediv_3" (type
TFOpLambda): • x=tf.Tensor(shape=(None, None, None, 3),
dtype=float16) • y=tf.Tensor(shape=(3,), dtype=float32) •
name=None
this is code =
from tensorflow.keras import layers
# Create base model
mixed_precision.set_global_policy(policy="mixed_float16")
input_shape = (224, 224, 3)
base_model = tf.keras.applications.EfficientNetB0(include_top=False)
base_model.trainable = False # freeze base model layers
# Create Functional model
inputs = layers.Input(shape=input_shape, name="input_layer")
# Note: EfficientNetBX models have rescaling built-in but if your model didn't you could have a layer like below
# x = layers.Rescaling(1./255)(x)
x = base_model(inputs, training=False) # set base_model to inference mode only
x = layers.GlobalAveragePooling2D(name="pooling_layer")(x)
x = layers.Dense(len(class_names))(x) # want one output neuron per class
# Separate activation of output layer so we can output float32 activations
outputs = layers.Activation("softmax", dtype=tf.float32, name="softmax_float32")(x)
model = tf.keras.Model(inputs, outputs)
# Compile the model
model.compile(loss="sparse_categorical_crossentropy", # Use sparse_categorical_crossentropy when labels are *not* one-hot
optimizer=tf.keras.optimizers.Adam(),
metrics=["accuracy"])
When I change this line with float32 instead of mixed_float16,like
this mixed_precision.set_global_policy(policy="float32") the
error goes away. I want to use Mixed_precision, how can I do it?
I'm trying to write a custom activation function for use with Keras. I can not write it with tensorflow primitives as it does properly compute the derivative. I followed How to make a custom activation function with only Python in Tensorflow? and it works very we in creating a tensorflow function. However, when I tried putting it into Keras as an activation function for the classic MNIST demo. I got errors. I also tried the tf_spiky function from the above reference.
Here is the sample code
tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf_spiky),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)])
Here's my entire error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-48-73a57f81db19> in <module>
3 tf.keras.layers.Dense(512, activation=tf_spiky),
4 tf.keras.layers.Dropout(0.2),
----> 5 tf.keras.layers.Dense(10, activation=tf.nn.softmax)])
6 x=tf.keras.layers.Activation(tf_spiky)
7 y=tf.keras.layers.Flatten(input_shape=(28, 28))
/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/checkpointable/base.py in _method_wrapper(self, *args, **kwargs)
472 self._setattr_tracking = False # pylint: disable=protected-access
473 try:
--> 474 method(self, *args, **kwargs)
475 finally:
476 self._setattr_tracking = previous_value # pylint: disable=protected-access
/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py in __init__(self, layers, name)
106 if layers:
107 for layer in layers:
--> 108 self.add(layer)
109
110 #property
/opt/conda/lib/python3.6/site-packages/tensorflow/python/training/checkpointable/base.py in _method_wrapper(self, *args, **kwargs)
472 self._setattr_tracking = False # pylint: disable=protected-access
473 try:
--> 474 method(self, *args, **kwargs)
475 finally:
476 self._setattr_tracking = previous_value # pylint: disable=protected-access
/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py in add(self, layer)
173 # If the model is being built continuously on top of an input layer:
174 # refresh its output.
--> 175 output_tensor = layer(self.outputs[0])
176 if isinstance(output_tensor, list):
177 raise TypeError('All layers in a Sequential model '
/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
728
729 # Check input assumptions set before layer building, e.g. input rank.
--> 730 self._assert_input_compatibility(inputs)
731 if input_list and self._dtype is None:
732 try:
/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in _assert_input_compatibility(self, inputs)
1463 if x.shape.ndims is None:
1464 raise ValueError('Input ' + str(input_index) + ' of layer ' +
-> 1465 self.name + ' is incompatible with the layer: '
1466 'its rank is undefined, but the layer requires a '
1467 'defined rank.')
ValueError: Input 0 of layer dense_1 is incompatible with the layer: its rank is undefined, but the layer requires a defined rank.
From this I gather the last Dense layer is unable to get the dimensions of the output after the activation function or something to that. I did see in the tensorflow code that many activation functions register a shape. But either I'm not doing that correctly or I'm going in the wrong direction. But I'm guessing something needs to be done to the tensorflow function to make it an activation function that Keras can use.
I would appreciate any help you can give.
As requested here is the sample codes for tf_spiky, it works as described in the above reference. However, once put into Keras I get the errors shown. This is pretty much as shown in the *How to make a custom activation function with only Python in Tensorflow?" stackoverflow article.
def spiky(x):
print(x)
r = x % 1
if r <= 0.5:
return r
else:
return 0
def d_spiky(x):
r = x % 1
if r <= 0.5:
return 1
else:
return 0
np_spiky = np.vectorize(spiky)
np_d_spiky = np.vectorize(d_spiky)
np_d_spiky_32 = lambda x: np_d_spiky(x).astype(np.float32)
import tensorflow as tf
from tensorflow.python.framework import ops
def tf_d_spiky(x,name=None):
with tf.name_scope(name, "d_spiky", [x]) as name:
y = tf.py_func(np_d_spiky_32,
[x],
[tf.float32],
name=name,
stateful=False)
return y[0]
def py_func(func, inp, Tout, stateful=True, name=None, grad=None):
# Need to generate a unique name to avoid duplicates:
rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
tf.RegisterGradient(rnd_name)(grad) # see _MySquareGrad for grad example
g = tf.get_default_graph()
with g.gradient_override_map({"PyFunc": rnd_name}):
return tf.py_func(func, inp, Tout, stateful=stateful, name=name)
def spikygrad(op, grad):
x = op.inputs[0]
n_gr = tf_d_spiky(x)
return grad * n_gr
np_spiky_32 = lambda x: np_spiky(x).astype(np.float32)
def tf_spiky(x, name=None):
with tf.name_scope(name, "spiky", [x]) as name:
y = py_func(np_spiky_32,
[x],
[tf.float32],
name=name,
grad=spikygrad) # <-- here's the call to the gradient
return y[0]
The solution is in this post Output from TensorFlow `py_func` has unknown rank/shape
The easiest fix is to add y[0].set_shape(x.get_shape()) before the return statement in the definition of tf_spiky.
Perhaps someone out there knows how to properly work with tensorflow shape functions. Digging around I found a unchanged_shape shape function in tensorflow.python.framework.common_shapes, which be appropriate here, but I don't know how to attach it to the tf_spiky function. Seems a python decorator is in order here. It would probably be a service to others to explain customizing tensorflow functions with shape functions.
I am trying to configure a model that I previously trained to classify images in a such a way that it accepts images as base64-strings (instead of a NumPy array), converts them to a NumPy array and then performs the prediction. How do I add a layer on top of my regular input layer that accepts strings and outputs a NumPy array?
So I've already pre-trained a model that predicts images based on the ResNet architecture. Having looked at this and this answer, I am trying to create a Lambda layer that converts strings to RGB jpeg images. I have done this as shown in the sample code below:
image = tf.placeholder(shape=[], dtype=tf.string)
input_tensor = keras.layers.Input(shape = (1,), tensor = image, dtype=tf.string)
x = keras.layers.Lambda(lambda image: tf.image.decode_jpeg(image))(input_tensor)
output_tensor = model(x)
new_model = Model(input_tensor, output_tensor)
Where model() is the Keras keras.models.Model model that I have pre-trained.
I am expecting new_model() to be the new Keras model that has 1 extra layer on top of my previous model, which accepts base64-string and outputs a NumPy array into the next layer.
However, the third line of my code raises the following error:
TypeError: Input 'contents' of 'DecodeJpeg' Op has type float32 that does not match expected type of string.
My understanding of this is that the 'image' in the Lambda layer that uses the decode_jpeg() is a float32 instead of a string, which seems odd to me as I have set the dtype of both the placeholder as well as the Input layer to tf.string.
I have searched all over stackoverflow for this but can't find a solution for this error. It appears this question also has not been able to find a solution for this specific issue.
EDIT 1: corrected typo and added full error message
The full error message is show below:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
509 as_ref=input_arg.is_ref,
--> 510 preferred_dtype=default_dtype)
511 except TypeError as err:
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx)
1103 if ret is None:
-> 1104 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1105
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in _TensorTensorConversionFunction(t, dtype, name, as_ref)
946 "Tensor conversion requested dtype %s for Tensor with dtype %s: %r" %
--> 947 (dtype.name, t.dtype.name, str(t)))
948 return t
ValueError: Tensor conversion requested dtype string for Tensor with dtype float32: 'Tensor("lambda_28/Placeholder:0", shape=(?, 1), dtype=float32)'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-47-5793b0703860> in <module>
1 image = tf.placeholder(shape=[], dtype=tf.string)
2 input_tensor = Input(shape = (1,), tensor = image, dtype=tf.string)
----> 3 x = Lambda(lambda image: tf.image.decode_jpeg(image))(input_tensor)
4 output_tensor = model(x)
5
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
472 if all([s is not None
473 for s in to_list(input_shape)]):
--> 474 output_shape = self.compute_output_shape(input_shape)
475 else:
476 if isinstance(input_shape, list):
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/layers/core.py in compute_output_shape(self, input_shape)
650 else:
651 x = K.placeholder(shape=input_shape)
--> 652 x = self.call(x)
653 if isinstance(x, list):
654 return [K.int_shape(x_elem) for x_elem in x]
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/keras/layers/core.py in call(self, inputs, mask)
685 if has_arg(self.function, 'mask'):
686 arguments['mask'] = mask
--> 687 return self.function(inputs, **arguments)
688
689 def compute_mask(self, inputs, mask=None):
<ipython-input-47-5793b0703860> in <lambda>(image)
1 image = tf.placeholder(shape=[], dtype=tf.string)
2 input_tensor = Input(shape = (1,), tensor = image, dtype=tf.string)
----> 3 x = Lambda(lambda image: tf.image.decode_jpeg(image))(input_tensor)
4 output_tensor = model(x)
5
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/gen_image_ops.py in decode_jpeg(contents, channels, ratio, fancy_upscaling, try_recover_truncated, acceptable_fraction, dct_method, name)
946 try_recover_truncated=try_recover_truncated,
947 acceptable_fraction=acceptable_fraction, dct_method=dct_method,
--> 948 name=name)
949 _result = _op.outputs[:]
950 _inputs_flat = _op.inputs
~/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
531 if input_arg.type != types_pb2.DT_INVALID:
532 raise TypeError("%s expected type of %s." %
--> 533 (prefix, dtypes.as_dtype(input_arg.type).name))
534 else:
535 # Update the maps with the default, if needed.
TypeError: Input 'contents' of 'DecodeJpeg' Op has type float32 that does not match expected type of string.
I am trying to do a large multiclass classification(Actually a translation).
I am trying to use tensorflow nce_loss in keras, but not able to make it work. Any help here?
I am not sure how can I pass weights,num_class and bias from previous layer to nce_loss.
I am getting following error:
import tensorflow as tf
from attention_decoder import AttentionDecoder
from keras.layers import Dropout,Masking,Embedding
def keras_nce_loss(tgt, pred):
return tf.nn.nce_loss(labels=tgt,inputs=pred,num_sampled=100)
model2 = Sequential()
model2.add(Embedding(input_features, input_embed_dimension, input_length=n_timesteps_in,mask_zero=True))
model2.add(Dropout(0.2))
model2.add(LSTM(LSTM_Unitsize,return_sequences=True,activation='relu'))
model2.add(Masking(mask_value=0.))
model2.add(AttentionDecoder(LSTM_Unitsize, n_features))
model2.compile(loss=keras_nce_loss, optimizer='adam', metrics=['acc'])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-157-0d76d4053a42> in <module>()
11 model2.add(Masking(mask_value=0.))
12 model2.add(AttentionDecoder(LSTM_Unitsize, n_features))
---> 13 model2.compile(loss=keras_nce_loss, optimizer='adam', metrics=['acc'])
14 #model2.save("model2_compiled.hd5")
/usr/local/lib/python3.6/dist-packages/keras/models.py in compile(self, optimizer, loss, metrics, sample_weight_mode, **kwargs)
786 metrics=metrics,
787 sample_weight_mode=sample_weight_mode,
--> 788 **kwargs)
789 self.optimizer = self.model.optimizer
790 self.loss = self.model.loss
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, **kwargs)
909 loss_weight = loss_weights_list[i]
910 output_loss = weighted_loss(y_true, y_pred,
--> 911 sample_weight, mask)
912 if len(self.outputs) > 1:
913 self.metrics_tensors.append(output_loss)
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in weighted(y_true, y_pred, weights, mask)
434 """
435 # score_array has ndim >= 2
--> 436 score_array = fn(y_true, y_pred)
437 if mask is not None:
438 # Cast the mask to floatX to avoid float64 upcasting in theano
<ipython-input-155-ec20de882530> in keras_nce_loss(tgt, pred)
2
3 def keras_nce_loss(tgt, pred):
----> 4 return tf.nn.nce_loss(labels=tgt,inputs=pred,num_sampled=100)
TypeError: nce_loss() missing 3 required positional arguments: 'weights', 'biases', and 'num_classes'