Binary classification of a sequance of vectors using Keras
$begingroup$
I have a list of vectors (shape of one vector = (1, 1000)), and I want to use something like LSTM to predict if a sequence is legal or not.
I'm trying to following architecture:
number_of_vectors = 2
x_in = Input(shape=(1, number_of_vectors * 1000))
x = LSTM(100, dropout=0.2, recurrent_dropout=0.2)(x_in)
x = Dense(1, activation='sigmoid')(x)
model = Model(input=x_in, output=x)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
Each data sample is a list containing 2 ndarrays (of the vectors), and a binary value (legal/illegal).
What are the steps needed to pre-process the data, and correct the model's architecture, in order to make this work ?
classification keras lstm binary vector-space-models
asked 4 hours ago
Miko DikoMiko Diko
1062
$endgroup$
add a comment |
$begingroup$
I have a list of vectors (shape of one vector = (1, 1000)), and I want to use something like LSTM to predict if a sequence is legal or not.
I'm trying to following architecture:
number_of_vectors = 2
x_in = Input(shape=(1, number_of_vectors * 1000))
x = LSTM(100, dropout=0.2, recurrent_dropout=0.2)(x_in)
x = Dense(1, activation='sigmoid')(x)
model = Model(input=x_in, output=x)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
Each data sample is a list containing 2 ndarrays (of the vectors), and a binary value (legal/illegal).
What are the steps needed to pre-process the data, and correct the model's architecture, in order to make this work ?
classification keras lstm binary vector-space-models
asked 4 hours ago
Miko DikoMiko Diko
1062
$endgroup$
add a comment |
$begingroup$
I have a list of vectors (shape of one vector = (1, 1000)), and I want to use something like LSTM to predict if a sequence is legal or not.
I'm trying to following architecture:
number_of_vectors = 2
x_in = Input(shape=(1, number_of_vectors * 1000))
x = LSTM(100, dropout=0.2, recurrent_dropout=0.2)(x_in)
x = Dense(1, activation='sigmoid')(x)
model = Model(input=x_in, output=x)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
Each data sample is a list containing 2 ndarrays (of the vectors), and a binary value (legal/illegal).
What are the steps needed to pre-process the data, and correct the model's architecture, in order to make this work ?
classification keras lstm binary vector-space-models
asked 4 hours ago
Miko DikoMiko Diko
1062
$endgroup$
I have a list of vectors (shape of one vector = (1, 1000)), and I want to use something like LSTM to predict if a sequence is legal or not.
I'm trying to following architecture:
number_of_vectors = 2
x_in = Input(shape=(1, number_of_vectors * 1000))
x = LSTM(100, dropout=0.2, recurrent_dropout=0.2)(x_in)
x = Dense(1, activation='sigmoid')(x)
model = Model(input=x_in, output=x)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
Each data sample is a list containing 2 ndarrays (of the vectors), and a binary value (legal/illegal).
What are the steps needed to pre-process the data, and correct the model's architecture, in order to make this work ?
classification keras lstm binary vector-space-models
classification keras lstm binary vector-space-models
asked 4 hours ago
Miko DikoMiko Diko
1062
asked 4 hours ago
Miko DikoMiko Diko
1062
asked 4 hours ago
Miko DikoMiko Diko
1062
asked 4 hours ago
Miko DikoMiko Diko
1062
asked 4 hours ago
Miko DikoMiko Diko
1062
1062
add a comment |
add a comment |
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