input_shape definition in a dense layer when input is an array
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0
down vote
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I have an input array, for deep learning classifier, looking like this:
[[ a1, [b1,c1]
[d1,e1]
[f1,g1]]
[ a2, [b2,c2]
[d2,e2]
[f2,g2]]
[h2,i2]]
.......]
So each set has one float and one array (N, 2) where N is not the same for each set.
When googling I noticed that I can input multiple sizes into input_shape value so I tried:
input_shape=(1,(2,None)) /None means undefined size
I tried changing order but it didn't help. Every time I change input_dim to input_shape I have:
TypeError: Error converting shape to a TensorShape: int() argument must be a string, a bytes-like object or a number, not 'tuple'.
How should I define input dim in my case? Thanks!
Code:
classifier = Sequential()
classifier.add(Dense(output_dim = 10, init = 'uniform', activation = 'relu', input_shape=(1,(2,None))))
classifier.add(Dense(output_dim = 10, init = 'uniform', activation = 'relu'))
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = 'Adamax', loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
python tensorflow machine-learning keras deep-learning
asked 9 hours ago
Krzychu111
92
add a comment |
up vote
0
down vote
favorite
I have an input array, for deep learning classifier, looking like this:
[[ a1, [b1,c1]
[d1,e1]
[f1,g1]]
[ a2, [b2,c2]
[d2,e2]
[f2,g2]]
[h2,i2]]
.......]
So each set has one float and one array (N, 2) where N is not the same for each set.
When googling I noticed that I can input multiple sizes into input_shape value so I tried:
input_shape=(1,(2,None)) /None means undefined size
I tried changing order but it didn't help. Every time I change input_dim to input_shape I have:
TypeError: Error converting shape to a TensorShape: int() argument must be a string, a bytes-like object or a number, not 'tuple'.
How should I define input dim in my case? Thanks!
Code:
classifier = Sequential()
classifier.add(Dense(output_dim = 10, init = 'uniform', activation = 'relu', input_shape=(1,(2,None))))
classifier.add(Dense(output_dim = 10, init = 'uniform', activation = 'relu'))
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = 'Adamax', loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
python tensorflow machine-learning keras deep-learning
asked 9 hours ago
Krzychu111
92
You will need a model with two separate inputs. This is not possible with the Sequential API but requires the Funtional API. Have a look at the docs for some examples. In addition, you will need to pass your second input to a layer that can handle the varying size, such as an LSTM or an Embedding layer.
– sdcbr
9 hours ago
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I have an input array, for deep learning classifier, looking like this:
[[ a1, [b1,c1]
[d1,e1]
[f1,g1]]
[ a2, [b2,c2]
[d2,e2]
[f2,g2]]
[h2,i2]]
.......]
So each set has one float and one array (N, 2) where N is not the same for each set.
When googling I noticed that I can input multiple sizes into input_shape value so I tried:
input_shape=(1,(2,None)) /None means undefined size
I tried changing order but it didn't help. Every time I change input_dim to input_shape I have:
TypeError: Error converting shape to a TensorShape: int() argument must be a string, a bytes-like object or a number, not 'tuple'.
How should I define input dim in my case? Thanks!
Code:
classifier = Sequential()
classifier.add(Dense(output_dim = 10, init = 'uniform', activation = 'relu', input_shape=(1,(2,None))))
classifier.add(Dense(output_dim = 10, init = 'uniform', activation = 'relu'))
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = 'Adamax', loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
python tensorflow machine-learning keras deep-learning
asked 9 hours ago
Krzychu111
92
I have an input array, for deep learning classifier, looking like this:
[[ a1, [b1,c1]
[d1,e1]
[f1,g1]]
[ a2, [b2,c2]
[d2,e2]
[f2,g2]]
[h2,i2]]
.......]
So each set has one float and one array (N, 2) where N is not the same for each set.
When googling I noticed that I can input multiple sizes into input_shape value so I tried:
input_shape=(1,(2,None)) /None means undefined size
I tried changing order but it didn't help. Every time I change input_dim to input_shape I have:
TypeError: Error converting shape to a TensorShape: int() argument must be a string, a bytes-like object or a number, not 'tuple'.
How should I define input dim in my case? Thanks!
Code:
classifier = Sequential()
classifier.add(Dense(output_dim = 10, init = 'uniform', activation = 'relu', input_shape=(1,(2,None))))
classifier.add(Dense(output_dim = 10, init = 'uniform', activation = 'relu'))
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = 'Adamax', loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
python tensorflow machine-learning keras deep-learning
python tensorflow machine-learning keras deep-learning
asked 9 hours ago
Krzychu111
92
asked 9 hours ago
Krzychu111
92
asked 9 hours ago
Krzychu111
92
asked 9 hours ago
Krzychu111
92
asked 9 hours ago
Krzychu111
92
92
You will need a model with two separate inputs. This is not possible with the Sequential API but requires the Funtional API. Have a look at the docs for some examples. In addition, you will need to pass your second input to a layer that can handle the varying size, such as an LSTM or an Embedding layer.
– sdcbr
9 hours ago
add a comment |
You will need a model with two separate inputs. This is not possible with the Sequential API but requires the Funtional API. Have a look at the docs for some examples. In addition, you will need to pass your second input to a layer that can handle the varying size, such as an LSTM or an Embedding layer.
– sdcbr
9 hours ago
You will need a model with two separate inputs. This is not possible with the Sequential API but requires the Funtional API. Have a look at the docs for some examples. In addition, you will need to pass your second input to a layer that can handle the varying size, such as an LSTM or an Embedding layer.
– sdcbr
9 hours ago
You will need a model with two separate inputs. This is not possible with the Sequential API but requires the Funtional API. Have a look at the docs for some examples. In addition, you will need to pass your second input to a layer that can handle the varying size, such as an LSTM or an Embedding layer.
– sdcbr
9 hours ago
add a comment |
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You will need a model with two separate inputs. This is not possible with the Sequential API but requires the Funtional API. Have a look at the docs for some examples. In addition, you will need to pass your second input to a layer that can handle the varying size, such as an LSTM or an Embedding layer.
– sdcbr
9 hours ago