问题
I have the following idea to implement:
Input -> CNN-> LSTM -> Dense -> Output
The Input has 100 time steps, each step has a 64-dimensional feature vector
A Conv1D layer will extract features at each time step. The CNN layer contains 64 filters, each has length 16 taps. Then, a maxpooling layer will extract the single maximum value of each convolutional output, so a total of 64 features will be extracted at each time step.
Then, the output of the CNN layer will be fed into an LSTM layer with 64 neurons. Number of recurrence is the same as time step of input, which is 100 time steps. The LSTM layer should return a sequence of 64-dimensional output (the length of sequence == number of time steps == 100, so there should be 100*64=6400 numbers).
input = Input(shape=(100,64), dtype='float', name='mfcc_input')
CNN_out = TimeDistributed(Conv1D(64, 16, activation='relu'))(mfcc_input)
CNN_out = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True)(CNN_out)
CNN_out = TimeDistributed(MaxPooling1D(pool_size=(64-16+1), strides=None, padding='valid'))(CNN_out)
LSTM_out = LSTM(64,return_sequences=True)(CNN_out)
... (more code) ...
But this doesn't work. The second line reports "list index out of range" and I don't understand what's going on.
I'm new to Keras, so I appreciate sincerely if anyone could help me with it.
This picture explains how CNN should be applied to EACH TIME STEP
回答1:
The problem is with your input. Your input is of shape (100, 64) in which the first dimension is the timesteps. So ignoring that, your input is of shape (64) to a Conv1D.
Now, refer to the Keras Conv1D documentation, which states that the input should be a 3D tensor (batch_size, steps, input_dim). Ignoring the batch_size, your input should be a 2D tensor (steps, input_dim).
So, you are providing 1D tensor input, where the expected size of the input is a 2D tensor. For example, if you are providing Natural Language input to the Conv1D in form of words, then there are 64 words in your sentence and supposing each word is encoded with a vector of length 50, your input should be (64, 50).
Also, make sure that you are feeding the right input to LSTM as given in the code below.
So, the correct code should be
embedding_size = 50 # Set this accordingingly
mfcc_input = Input(shape=(100, 64, embedding_size), dtype='float', name='mfcc_input')
CNN_out = TimeDistributed(Conv1D(64, 16, activation='relu'))(mfcc_input)
CNN_out = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True)(CNN_out)
CNN_out = TimeDistributed(MaxPooling1D(pool_size=(64-16+1), strides=None, padding='valid'))(CNN_out)
# Directly feeding CNN_out to LSTM will also raise Error, since the 3rd dimension is 1, you need to purge it as
CNN_out = Reshape((int(CNN_out.shape[1]), int(CNN_out.shape[3])))(CNN_out)
LSTM_out = LSTM(64,return_sequences=True)(CNN_out)
... (more code) ...
来源：https://stackoverflow.com/questions/49870386/how-to-setup-input-shape-for-1dcnnlstm-network-keras