CODEWITHSUNDEEP
pythonneural-networkdeep-learningkeraslstm
jef
jef

Reputation: 4083

Keras LSTM: Error when checking model input dimension

I am a new user of keras, and trying to implement a LSTM model. For test I declared the model like below, but it fails because of difference of input dimension. Although I found similar problems in this site, I could not find my mistakes by myself. 

ValueError: 
Error when checking model input: 
expected lstm_input_4 to have 3 dimensions, but got array with shape (300, 100)

My environment

Code

from keras.layers import Input, Dense
from keras.models import Sequential
from keras.layers import LSTM
from keras.optimizers import RMSprop, Adadelta
from keras.layers.wrappers import TimeDistributed
import numpy as np

in_size = 100
out_size = 10
nb_hidden = 8

model = Sequential()
model.add(LSTM(nb_hidden, 
               name='lstm',
               activation='tanh',
               return_sequences=True,
               input_shape=(None, in_size)))
model.add(TimeDistributed(Dense(out_size, activation='softmax')))

adadelta = Adadelta(clipnorm=1.)
model.compile(optimizer=adadelta,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# create dummy data
data_size = 300
train = np.zeros((data_size, in_size,), dtype=np.float32)
labels = np.zeros((data_size, out_size,), dtype=np.float32)
model.fit(train, labels)

Edit 1 (not working, after Marcin Możejko's comment)

Thank you Marcin Możejko. But I have a similar error like below. I updated dummy data for check. What is wrong of this code?

ValueError: Error when checking model target: expected timedistributed_36 to have 3 dimensions, but got array with shape (208, 1)

def create_dataset(X, Y, loop_back=1):
    dataX, dataY = [], []
    for i in range(len(X) - loop_back-1):
        a = X[i:(i+loop_back), :]
        dataX.append(a)
        dataY.append(Y[i+loop_back, :])
    return np.array(dataX), np.array(dataY)

data_size = 300
dataset = np.zeros((data_size, feature_size), dtype=np.float32)
dataset_labels = np.zeros((data_size, 1), dtype=np.float32)

train_size = int(data_size * 0.7)
trainX = dataset[0:train_size, :]
trainY = dataset_labels[0:train_size, :]
testX = dataset[train_size:, :]
testY = dataset_labels[train_size:, 0]
trainX, trainY = create_dataset(trainX, trainY)
print(trainX.shape, trainY.shape) # (208, 1, 1) (208, 1)

# in_size = 100
feature_size = 1
out_size = 1
nb_hidden = 8

model = Sequential()
model.add(LSTM(nb_hidden, 
               name='lstm',
               activation='tanh',
               return_sequences=True,
               input_shape=(1, feature_size)))

model.add(TimeDistributed(Dense(out_size, activation='softmax')))
adadelta = Adadelta(clipnorm=1.)
model.compile(optimizer=adadelta,
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(trainX, trainY, nb_epoch=10, batch_size=1)

Upvotes: 3

Views: 2106

Answers (1)

Marcin Możejko
Marcin Możejko

Reputation: 40516

This is a really classic problem with LSTM in Keras. LSTM input shape should be 2d - with shape (sequence_length, nb_of_features). Additional third dimension comes from examples dimension - so the table fed to model has shape (nb_of_examples, sequence_length, nb_of_features). This is where your problem comes from. Remember that a 1-d sequence should be presented as a 2-d array with shape (sequence_length, 1). This should be a input shape of your LSTM:

model.add(LSTM(nb_hidden, 
           name='lstm',
           activation='tanh',
           return_sequences=True,
           input_shape=(in_size, 1)))

And remember to reshape your input to appropriate format.

Upvotes: 3

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