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How do I cast properly a boolean layer in keras?
I am working with some custom layers and having problems with the shape of them, when I work with it separately it works, but when I load the model to use in another one as a layer, it doesn't work anymore. Here is my layers definition:
def signumTransform(x):
"""
SIGNUM function
if positive 1
if negative -1
"""
import keras.backend
return keras.backend.sign(x)
def logical_or_layer(x):
"""Processing an OR operation"""
import keras.backend
#normalized to 0,1
aux_array = keras.backend.sign(x)
aux_array = keras.backend.relu(aux_array)
# OR operation
aux_array = keras.backend.any(aux_array)
# casting back the True/False to 1,0
aux_array = keras.backend.cast(aux_array, dtype='float32')
return aux_array
#this is the input tensor
inputs = Input(shape=(inputSize,), name='input')
#this is the Neurule layer
x = Dense(neurulesQt, activation='softsign', name='neurules')(inputs)
#after each neuron layer, the outputs need to be put into SIGNUM (-1 or 1)
x = Lambda(signumTransform, output_shape=lambda x:x, name='signumAfterNeurules')(x)
#separating into 2 (2 possible outputs)
layer_split0 = Lambda( lambda x: x[:, :11], output_shape=[11], name='layer_split0')(x)
layer_split1 = Lambda( lambda x: x[:, 11:20], output_shape=[9], name='layer_split1')(x)
#this is the OR layer
y_0 = Lambda(logical_or_layer, output_shape=[1], name='or0')(layer_split0)
y_1 = Lambda(logical_or_layer, output_shape=[1], name='or1')(layer_split1)
y = Lambda(lambda x: K.stack([x[0], x[1]]),output_shape=[2], name="output")([y_0, y_1])
Until the layer_split everything works properly, but in my y_0 and y_1 I need to do an OR operation with keras.backend.any(), as a return I receive a boolean so I cast it back with keras.backend.cast().
If I use the Model as it is here described, it works...it compiles, can be validated and so on, but if I try to save it and load it, it simply loses one dimension, the batch dimension (None). The output in the summary is shown as (None, 2), but when used as a layer and concatenated with another one, it shows (2,) and an error is thrown:
InvalidArgumentError: Shape must be rank 2 but is rank 1 for 'merging_layer_10/concat' (op: 'ConcatV2') with input shapes: [?,16], [2], .
How should I properly cast it in the logical_or_layer function? Should I change the output_shape in the Lambda Layer?
python tensorflow lambda casting keras
asked Nov 22 '18 at 12:32
ViniciusVinicius
158
add a comment |
I am working with some custom layers and having problems with the shape of them, when I work with it separately it works, but when I load the model to use in another one as a layer, it doesn't work anymore. Here is my layers definition:
def signumTransform(x):
"""
SIGNUM function
if positive 1
if negative -1
"""
import keras.backend
return keras.backend.sign(x)
def logical_or_layer(x):
"""Processing an OR operation"""
import keras.backend
#normalized to 0,1
aux_array = keras.backend.sign(x)
aux_array = keras.backend.relu(aux_array)
# OR operation
aux_array = keras.backend.any(aux_array)
# casting back the True/False to 1,0
aux_array = keras.backend.cast(aux_array, dtype='float32')
return aux_array
#this is the input tensor
inputs = Input(shape=(inputSize,), name='input')
#this is the Neurule layer
x = Dense(neurulesQt, activation='softsign', name='neurules')(inputs)
#after each neuron layer, the outputs need to be put into SIGNUM (-1 or 1)
x = Lambda(signumTransform, output_shape=lambda x:x, name='signumAfterNeurules')(x)
#separating into 2 (2 possible outputs)
layer_split0 = Lambda( lambda x: x[:, :11], output_shape=[11], name='layer_split0')(x)
layer_split1 = Lambda( lambda x: x[:, 11:20], output_shape=[9], name='layer_split1')(x)
#this is the OR layer
y_0 = Lambda(logical_or_layer, output_shape=[1], name='or0')(layer_split0)
y_1 = Lambda(logical_or_layer, output_shape=[1], name='or1')(layer_split1)
y = Lambda(lambda x: K.stack([x[0], x[1]]),output_shape=[2], name="output")([y_0, y_1])
Until the layer_split everything works properly, but in my y_0 and y_1 I need to do an OR operation with keras.backend.any(), as a return I receive a boolean so I cast it back with keras.backend.cast().
If I use the Model as it is here described, it works...it compiles, can be validated and so on, but if I try to save it and load it, it simply loses one dimension, the batch dimension (None). The output in the summary is shown as (None, 2), but when used as a layer and concatenated with another one, it shows (2,) and an error is thrown:
InvalidArgumentError: Shape must be rank 2 but is rank 1 for 'merging_layer_10/concat' (op: 'ConcatV2') with input shapes: [?,16], [2], .
How should I properly cast it in the logical_or_layer function? Should I change the output_shape in the Lambda Layer?
python tensorflow lambda casting keras
asked Nov 22 '18 at 12:32
ViniciusVinicius
158
tryy = Concatenate( axis = -1, name = "output")([y_0,y_1])
– Mete Han Kahraman
Nov 22 '18 at 12:58
just changed a little yours toy = concatenate(inputs=[y_0, y_1], axis = -1, name = "output")and tried here, but this error is thrownInvalidArgumentError: Can't concatenate scalars (use tf.stack instead) for 'output_2/concat' (op: 'ConcatV2') with input shapes: , , .
– Vinicius
Nov 22 '18 at 13:05
add a comment |
I am working with some custom layers and having problems with the shape of them, when I work with it separately it works, but when I load the model to use in another one as a layer, it doesn't work anymore. Here is my layers definition:
def signumTransform(x):
"""
SIGNUM function
if positive 1
if negative -1
"""
import keras.backend
return keras.backend.sign(x)
def logical_or_layer(x):
"""Processing an OR operation"""
import keras.backend
#normalized to 0,1
aux_array = keras.backend.sign(x)
aux_array = keras.backend.relu(aux_array)
# OR operation
aux_array = keras.backend.any(aux_array)
# casting back the True/False to 1,0
aux_array = keras.backend.cast(aux_array, dtype='float32')
return aux_array
#this is the input tensor
inputs = Input(shape=(inputSize,), name='input')
#this is the Neurule layer
x = Dense(neurulesQt, activation='softsign', name='neurules')(inputs)
#after each neuron layer, the outputs need to be put into SIGNUM (-1 or 1)
x = Lambda(signumTransform, output_shape=lambda x:x, name='signumAfterNeurules')(x)
#separating into 2 (2 possible outputs)
layer_split0 = Lambda( lambda x: x[:, :11], output_shape=[11], name='layer_split0')(x)
layer_split1 = Lambda( lambda x: x[:, 11:20], output_shape=[9], name='layer_split1')(x)
#this is the OR layer
y_0 = Lambda(logical_or_layer, output_shape=[1], name='or0')(layer_split0)
y_1 = Lambda(logical_or_layer, output_shape=[1], name='or1')(layer_split1)
y = Lambda(lambda x: K.stack([x[0], x[1]]),output_shape=[2], name="output")([y_0, y_1])
Until the layer_split everything works properly, but in my y_0 and y_1 I need to do an OR operation with keras.backend.any(), as a return I receive a boolean so I cast it back with keras.backend.cast().
If I use the Model as it is here described, it works...it compiles, can be validated and so on, but if I try to save it and load it, it simply loses one dimension, the batch dimension (None). The output in the summary is shown as (None, 2), but when used as a layer and concatenated with another one, it shows (2,) and an error is thrown:
InvalidArgumentError: Shape must be rank 2 but is rank 1 for 'merging_layer_10/concat' (op: 'ConcatV2') with input shapes: [?,16], [2], .
How should I properly cast it in the logical_or_layer function? Should I change the output_shape in the Lambda Layer?
python tensorflow lambda casting keras
asked Nov 22 '18 at 12:32
ViniciusVinicius
158
I am working with some custom layers and having problems with the shape of them, when I work with it separately it works, but when I load the model to use in another one as a layer, it doesn't work anymore. Here is my layers definition:
def signumTransform(x):
"""
SIGNUM function
if positive 1
if negative -1
"""
import keras.backend
return keras.backend.sign(x)
def logical_or_layer(x):
"""Processing an OR operation"""
import keras.backend
#normalized to 0,1
aux_array = keras.backend.sign(x)
aux_array = keras.backend.relu(aux_array)
# OR operation
aux_array = keras.backend.any(aux_array)
# casting back the True/False to 1,0
aux_array = keras.backend.cast(aux_array, dtype='float32')
return aux_array
#this is the input tensor
inputs = Input(shape=(inputSize,), name='input')
#this is the Neurule layer
x = Dense(neurulesQt, activation='softsign', name='neurules')(inputs)
#after each neuron layer, the outputs need to be put into SIGNUM (-1 or 1)
x = Lambda(signumTransform, output_shape=lambda x:x, name='signumAfterNeurules')(x)
#separating into 2 (2 possible outputs)
layer_split0 = Lambda( lambda x: x[:, :11], output_shape=[11], name='layer_split0')(x)
layer_split1 = Lambda( lambda x: x[:, 11:20], output_shape=[9], name='layer_split1')(x)
#this is the OR layer
y_0 = Lambda(logical_or_layer, output_shape=[1], name='or0')(layer_split0)
y_1 = Lambda(logical_or_layer, output_shape=[1], name='or1')(layer_split1)
y = Lambda(lambda x: K.stack([x[0], x[1]]),output_shape=[2], name="output")([y_0, y_1])
Until the layer_split everything works properly, but in my y_0 and y_1 I need to do an OR operation with keras.backend.any(), as a return I receive a boolean so I cast it back with keras.backend.cast().
If I use the Model as it is here described, it works...it compiles, can be validated and so on, but if I try to save it and load it, it simply loses one dimension, the batch dimension (None). The output in the summary is shown as (None, 2), but when used as a layer and concatenated with another one, it shows (2,) and an error is thrown:
InvalidArgumentError: Shape must be rank 2 but is rank 1 for 'merging_layer_10/concat' (op: 'ConcatV2') with input shapes: [?,16], [2], .
How should I properly cast it in the logical_or_layer function? Should I change the output_shape in the Lambda Layer?
python tensorflow lambda casting keras
python tensorflow lambda casting keras
asked Nov 22 '18 at 12:32
ViniciusVinicius
158
asked Nov 22 '18 at 12:32
ViniciusVinicius
158
asked Nov 22 '18 at 12:32
ViniciusVinicius
158
asked Nov 22 '18 at 12:32
ViniciusVinicius
158
asked Nov 22 '18 at 12:32
ViniciusVinicius
158
158
tryy = Concatenate( axis = -1, name = "output")([y_0,y_1])
– Mete Han Kahraman
Nov 22 '18 at 12:58
just changed a little yours toy = concatenate(inputs=[y_0, y_1], axis = -1, name = "output")and tried here, but this error is thrownInvalidArgumentError: Can't concatenate scalars (use tf.stack instead) for 'output_2/concat' (op: 'ConcatV2') with input shapes: , , .
– Vinicius
Nov 22 '18 at 13:05
add a comment |
tryy = Concatenate( axis = -1, name = "output")([y_0,y_1])
– Mete Han Kahraman
Nov 22 '18 at 12:58
just changed a little yours toy = concatenate(inputs=[y_0, y_1], axis = -1, name = "output")and tried here, but this error is thrownInvalidArgumentError: Can't concatenate scalars (use tf.stack instead) for 'output_2/concat' (op: 'ConcatV2') with input shapes: , , .
– Vinicius
Nov 22 '18 at 13:05
try
y = Concatenate( axis = -1, name = "output")([y_0,y_1])– Mete Han Kahraman
Nov 22 '18 at 12:58
try
y = Concatenate( axis = -1, name = "output")([y_0,y_1])– Mete Han Kahraman
Nov 22 '18 at 12:58
just changed a little yours to
y = concatenate(inputs=[y_0, y_1], axis = -1, name = "output") and tried here, but this error is thrown InvalidArgumentError: Can't concatenate scalars (use tf.stack instead) for 'output_2/concat' (op: 'ConcatV2') with input shapes: , , .– Vinicius
Nov 22 '18 at 13:05
just changed a little yours to
y = concatenate(inputs=[y_0, y_1], axis = -1, name = "output") and tried here, but this error is thrown InvalidArgumentError: Can't concatenate scalars (use tf.stack instead) for 'output_2/concat' (op: 'ConcatV2') with input shapes: , , .– Vinicius
Nov 22 '18 at 13:05
add a comment |
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try
y = Concatenate( axis = -1, name = "output")([y_0,y_1])– Mete Han Kahraman
Nov 22 '18 at 12:58
just changed a little yours to
y = concatenate(inputs=[y_0, y_1], axis = -1, name = "output")and tried here, but this error is thrownInvalidArgumentError: Can't concatenate scalars (use tf.stack instead) for 'output_2/concat' (op: 'ConcatV2') with input shapes: , , .– Vinicius
Nov 22 '18 at 13:05