ValueError: Input 0 is incompatible with layer lstm_13: expected ndim=3, found ndim=4
Question:
I am trying for multi-class classification and here are the details of my training input and output:
train_input.shape= (1, 95000, 360) (95000 length input array with each
element being an array of 360 length)
train_output.shape = (1, 95000, 22) (22 Classes are there)
model = Sequential()
model.add(LSTM(22, input_shape=(1, 95000,360)))
model.add(Dense(22, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(train_input, train_output, epochs=2, batch_size=500)
The error is:
ValueError: Input 0 is incompatible with layer lstm_13: expected ndim=3, found ndim=4
in line:
model.add(LSTM(22, input_shape=(1, 95000,360)))
Please help me out, I am not able to solve it through other answers.
Answers:
input_shape is supposed to be (timesteps, n_features). Remove the first dimension.
input_shape = (95000,360)
Same for the output.
I solved the problem by making
input size: (95000,360,1) and
output size: (95000,22)
and changed the input shape to (360,1) in the code where model is defined:
model = Sequential()
model.add(LSTM(22, input_shape=(360,1)))
model.add(Dense(22, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(ml2_train_input, ml2_train_output_enc, epochs=2, batch_size=500)
Well, I think the main problem out there is with the return_sequences parameter in the network.This hyper parameter should be set to False for the last layer and true for the other previous layers.
Adding up to the answers above,
return_sequences should not be set True in all layers, just do not set it in the last layer, and omit return_sequences=True
In Artifical Neural Networks (ANN), input is of shape (N,D), where N is the number of samples and D is the number of features.
IN RNN, GRU and LSTM, input is of shape (N,T,D), where N is the number of samples, T is length of time sequence and D is the number of features.
So, while adding layers
Input(shape = (D,)) for ANN and
Input(shape = (T,D)) for RNN, GRU and LSTMs
I am trying for multi-class classification and here are the details of my training input and output:
train_input.shape= (1, 95000, 360) (95000 length input array with each
element being an array of 360 length)train_output.shape = (1, 95000, 22) (22 Classes are there)
model = Sequential()
model.add(LSTM(22, input_shape=(1, 95000,360)))
model.add(Dense(22, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(train_input, train_output, epochs=2, batch_size=500)
The error is:
ValueError: Input 0 is incompatible with layer lstm_13: expected ndim=3, found ndim=4
in line:
model.add(LSTM(22, input_shape=(1, 95000,360)))
Please help me out, I am not able to solve it through other answers.
input_shape is supposed to be (timesteps, n_features). Remove the first dimension.
input_shape = (95000,360)
Same for the output.
I solved the problem by making
input size: (95000,360,1) and
output size: (95000,22)
and changed the input shape to (360,1) in the code where model is defined:
model = Sequential()
model.add(LSTM(22, input_shape=(360,1)))
model.add(Dense(22, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(ml2_train_input, ml2_train_output_enc, epochs=2, batch_size=500)
Well, I think the main problem out there is with the return_sequences parameter in the network.This hyper parameter should be set to False for the last layer and true for the other previous layers.
Adding up to the answers above,
return_sequences should not be set True in all layers, just do not set it in the last layer, and omit return_sequences=True
In Artifical Neural Networks (ANN), input is of shape (N,D), where N is the number of samples and D is the number of features.
IN RNN, GRU and LSTM, input is of shape (N,T,D), where N is the number of samples, T is length of time sequence and D is the number of features.
So, while adding layers
Input(shape = (D,)) for ANN and
Input(shape = (T,D)) for RNN, GRU and LSTMs