error when running the model.compile function
opened this issue · comments
when i run the following block of code from the tutorial i see an error.
model_config = Config(
textual_embedding_dim=500,
input_dim=len(word2index_x.keys()),
output_dim=len(word2index_y.keys()))
model = BlindBOW(model_config)
model.create()
model.compile(
loss='categorical_crossentropy',
optimizer='adam')
text_bow_model = modelTypeError Traceback (most recent call last)
in ()
9 model.compile(
10 loss='categorical_crossentropy',
---> 11 optimizer='adam')
12 text_bow_model = model
/home/bassel/anaconda/lib/python2.7/site-packages/keras/models.pyc in compile(self, optimizer, loss, class_mode, sample_weight_mode, **kwargs)
505 self.X_test = self.get_input(train=False)
506
--> 507 self.y_train = self.get_output(train=True)
508 self.y_test = self.get_output(train=False)
509
/home/bassel/anaconda/lib/python2.7/site-packages/keras/layers/containers.pyc in get_output(self, train)
128
129 def get_output(self, train=False):
--> 130 return self.layers[-1].get_output(train)
131
132 def set_input(self):
/home/bassel/anaconda/lib/python2.7/site-packages/keras/layers/core.pyc in get_output(self, train)
735
736 def get_output(self, train=False):
--> 737 X = self.get_input(train)
738 return self.activation(X)
739
/home/bassel/anaconda/lib/python2.7/site-packages/keras/layers/core.pyc in get_input(self, train)
239 if previous_layer_id in self.layer_cache:
240 return self.layer_cache[previous_layer_id]
--> 241 previous_output = self.previous.get_output(train=train)
242 if self.layer_cache is not None and self.cache_enabled:
243 previous_layer_id = '%s_%s' % (id(self.previous), train)
/home/bassel/anaconda/lib/python2.7/site-packages/keras/layers/core.pyc in get_output(self, train)
1028
1029 def get_output(self, train=False):
-> 1030 X = self.get_input(train)
1031 output = self.activation(K.dot(X, self.W) + self.b)
1032 return output
/home/bassel/anaconda/lib/python2.7/site-packages/keras/layers/core.pyc in get_input(self, train)
239 if previous_layer_id in self.layer_cache:
240 return self.layer_cache[previous_layer_id]
--> 241 previous_output = self.previous.get_output(train=train)
242 if self.layer_cache is not None and self.cache_enabled:
243 previous_layer_id = '%s_%s' % (id(self.previous), train)
/home/bassel/anaconda/lib/python2.7/site-packages/keras/layers/core.pyc in get_output(self, train)
701
702 def get_output(self, train=False):
--> 703 X = self.get_input(train)
704 if self.p > 0.:
705 if train:
/home/bassel/anaconda/lib/python2.7/site-packages/keras/layers/core.pyc in get_input(self, train)
239 if previous_layer_id in self.layer_cache:
240 return self.layer_cache[previous_layer_id]
--> 241 previous_output = self.previous.get_output(train=train)
242 if self.layer_cache is not None and self.cache_enabled:
243 previous_layer_id = '%s_%s' % (id(self.previous), train)
/home/bassel/anaconda/lib/python2.7/site-packages/keras/layers/core.pyc in get_output(self, train)
229
230 def get_output(self, train=False):
--> 231 return self.get_input(train)
232
233 def get_input(self, train=False):
/home/bassel/anaconda/lib/python2.7/site-packages/keras/layers/core.pyc in get_input(self, train)
239 if previous_layer_id in self.layer_cache:
240 return self.layer_cache[previous_layer_id]
--> 241 previous_output = self.previous.get_output(train=train)
242 if self.layer_cache is not None and self.cache_enabled:
243 previous_layer_id = '%s_%s' % (id(self.previous), train)
/home/bassel/Desktop/Graduation_project_2/visual_turing_test-tutorial-master/kraino/core/keras_extensions.py in get_output(self, train)
111 if hasattr(self, 'previous'):
112 return func(self.previous.get_output(train),
--> 113 self.previous.get_output_mask(train))
114 else:
115 return func(self.input, self.get_output_mask(train))
/home/bassel/Desktop/Graduation_project_2/visual_turing_test-tutorial-master/kraino/core/keras_extensions.py in time_distributed_masked_ave(x, m)
51 """
52 tmp = K.sum(x, axis=1)
---> 53 nonzeros = K.sum(m, axis=-1)
54 return tmp / K.expand_dims(K.cast(nonzeros, tmp.dtype))
55
/home/bassel/anaconda/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in sum(x, axis, keepdims)
149 '''
150 axis = normalize_axis(axis, ndim(x))
--> 151 return tf.reduce_sum(x, reduction_indices=axis, keep_dims=keepdims)
152
153
/home/bassel/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.pyc in reduce_sum(input_tensor, axis, keep_dims, name, reduction_indices)
1173 _ReductionDims(input_tensor, axis, reduction_indices),
1174 keep_dims,
-> 1175 name=name)
1176
1177
/home/bassel/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.pyc in _sum(input, reduction_indices, keep_dims, name)
2785 result = _op_def_lib.apply_op("Sum", input=input,
2786 reduction_indices=reduction_indices,
-> 2787 keep_dims=keep_dims, name=name)
2788 return result
2789
/home/bassel/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.pyc in apply_op(self, op_type_name, name, **keywords)
580 for base_type in base_types:
581 _SatisfiesTypeConstraint(base_type,
--> 582 _Attr(op_def, input_arg.type_attr))
583 attrs[input_arg.type_attr] = attr_value
584 inferred_from[input_arg.type_attr] = input_name
/home/bassel/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.pyc in _SatisfiesTypeConstraint(dtype, attr_def)
58 "DataType %s for attr '%s' not in list of allowed values: %s" %
59 (dtypes.as_dtype(dtype).name, attr_def.name,
---> 60 ", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
61
62
TypeError: DataType bool for attr 'T' not in list of allowed values: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, float16
Do you use the following configuration?
-
Python 2.7.3
-
Theano:0.8.0.dev0.dev-63990436c98f107cf120f3578021a5d259ecf352
-
Keras:b587aeee1c1be3633a56b945af3e7c2c303369ca
Please tell me how could i do that? i mean, how do i install those modules of specific configuration?
http://stackoverflow.com/questions/3555107/git-clone-particular-version-of-remote-repository
Hashes are these series of letters and digits. I hope this helps.
thanks, it worked.