How to shape one-hot data for keras lstm
0
$begingroup$
I am trying to learn how to make an LSTM in keras and I was having difficulty working out what input dimensions the data needs to be for the cudnnlstm layer. Each time I try to alter the input_shape I get an error about expecting 2 dimensions and getting 3. My code is:
from keras.models import Sequential
from keras.layers import CuDNNLSTM,Dense,Dropout
import keras
x = [np.random.randint(0,10000) for i in range(5000)]
x = keras.utils.to_categorical(x)
y = np.roll(x,-1)
classes = x.shape[1]
model = Sequential()
model.add(CuDNNLSTM(256,batch_size=50,input_shape=(5000,classes),return_sequences=True))
model.add(Dropout(0.2))
model.add(CuDNNLSTM(256))
model.add(Dropout(0.2))
model.add(Dense(256,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(Z-hidden_size,activation='softmax'))
opt = keras.optimizers.Adam(lr=1e-3,decay=1e-5)
model.compile(loss='sparse_categorical_crossentropy',optimizer=opt)
model.fit(x,y,epochs=3)
python keras lstm
asked 12 mins ago
treutmtreutm
174
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add a comment |
0
$begingroup$
I am trying to learn how to make an LSTM in keras and I was having difficulty working out what input dimensions the data needs to be for the cudnnlstm layer. Each time I try to alter the input_shape I get an error about expecting 2 dimensions and getting 3. My code is:
from keras.models import Sequential
from keras.layers import CuDNNLSTM,Dense,Dropout
import keras
x = [np.random.randint(0,10000) for i in range(5000)]
x = keras.utils.to_categorical(x)
y = np.roll(x,-1)
classes = x.shape[1]
model = Sequential()
model.add(CuDNNLSTM(256,batch_size=50,input_shape=(5000,classes),return_sequences=True))
model.add(Dropout(0.2))
model.add(CuDNNLSTM(256))
model.add(Dropout(0.2))
model.add(Dense(256,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(Z-hidden_size,activation='softmax'))
opt = keras.optimizers.Adam(lr=1e-3,decay=1e-5)
model.compile(loss='sparse_categorical_crossentropy',optimizer=opt)
model.fit(x,y,epochs=3)
python keras lstm
asked 12 mins ago
treutmtreutm
174
$endgroup$
add a comment |
0
0
0
$begingroup$
I am trying to learn how to make an LSTM in keras and I was having difficulty working out what input dimensions the data needs to be for the cudnnlstm layer. Each time I try to alter the input_shape I get an error about expecting 2 dimensions and getting 3. My code is:
from keras.models import Sequential
from keras.layers import CuDNNLSTM,Dense,Dropout
import keras
x = [np.random.randint(0,10000) for i in range(5000)]
x = keras.utils.to_categorical(x)
y = np.roll(x,-1)
classes = x.shape[1]
model = Sequential()
model.add(CuDNNLSTM(256,batch_size=50,input_shape=(5000,classes),return_sequences=True))
model.add(Dropout(0.2))
model.add(CuDNNLSTM(256))
model.add(Dropout(0.2))
model.add(Dense(256,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(Z-hidden_size,activation='softmax'))
opt = keras.optimizers.Adam(lr=1e-3,decay=1e-5)
model.compile(loss='sparse_categorical_crossentropy',optimizer=opt)
model.fit(x,y,epochs=3)
python keras lstm
asked 12 mins ago
treutmtreutm
174
$endgroup$
I am trying to learn how to make an LSTM in keras and I was having difficulty working out what input dimensions the data needs to be for the cudnnlstm layer. Each time I try to alter the input_shape I get an error about expecting 2 dimensions and getting 3. My code is:
from keras.models import Sequential
from keras.layers import CuDNNLSTM,Dense,Dropout
import keras
x = [np.random.randint(0,10000) for i in range(5000)]
x = keras.utils.to_categorical(x)
y = np.roll(x,-1)
classes = x.shape[1]
model = Sequential()
model.add(CuDNNLSTM(256,batch_size=50,input_shape=(5000,classes),return_sequences=True))
model.add(Dropout(0.2))
model.add(CuDNNLSTM(256))
model.add(Dropout(0.2))
model.add(Dense(256,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(Z-hidden_size,activation='softmax'))
opt = keras.optimizers.Adam(lr=1e-3,decay=1e-5)
model.compile(loss='sparse_categorical_crossentropy',optimizer=opt)
model.fit(x,y,epochs=3)
python keras lstm
python keras lstm
asked 12 mins ago
treutmtreutm
174
asked 12 mins ago
treutmtreutm
174
asked 12 mins ago
treutmtreutm
174
asked 12 mins ago
treutmtreutm
174
asked 12 mins ago
treutmtreutm
174
174
add a comment |
add a comment |
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