ValueError: Input 0 of layer sequential_40 is incompatible with the layer

Question

I'm modifying an old code, by adding attention layer to a model. But I'm not able to figure out how to stack the layers with correct input size.

The actual input data would be (200,189,1).

//I'm trying something like this

def mocap_model(optimizer='SGD'):
    model = Sequential()
    model.add(Conv2D(32, 3, strides=(2, 2), padding ='same', input_shape=(200, 189, 1)))
    model.add(Dropout(0.2))
    model.add(Activation('relu'))
    model.add(Conv2D(64, 3, strides=(2, 2), padding ='same'))
    model.add(Dropout(0.2))
    model.add(Activation('relu'))
    model.add(Conv2D(64, 3, strides=(2, 2), padding ='same'))
    model.add(Dropout(0.2))
    model.add(Activation('relu'))
    model.add(Conv2D(128, 3, strides=(2, 2), padding ='same'))
    model.add(Dropout(0.2))
    model.add(Flatten())

    return model

cnn = mocap_model()
    
main_input = Input(shape=(200, 189, 1))
    
rnn = Sequential()
rnn = LSTM(256, return_sequences=True, input_shape=(200,189))
    
model = TimeDistributed(cnn)(main_input) 
model = rnn(model)
    
att_in=LSTM(256,return_sequences=True,dropout=0.3,recurrent_dropout=0.2)(model)
att_out=attention()(att_in)
output3=Dense(256,activation='relu',trainable=True)(att_out)
output4=Dense(4,activation='softmax',trainable=True)(output3)
model=Model(main_input,output4)
    
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()

But I get this error:

----> 8 model = TimeDistributed(cnn)(main_input)

ValueError: Input 0 of layer sequential_40 is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: (None, 189, 1)

Answer 1

Problem with Input shape. tf.keras.layers.TimeDistributed expects batch size as input. Expects inputs: Input tensor of shape (batch, time, ...) . In the main_input add batch_size

main_input = Input(shape=(10, 200, 189, 1))

Working sample code

import tensorflow as tf

cnn = tf.keras.Sequential()
cnn.add(tf.keras.layers.Conv2D(64, 1, 1, input_shape=(200, 189, 1)))
cnn.add(tf.keras.layers.Flatten())
cnn.output_shape

main_input = tf.keras.Input(shape=(10, 200, 189, 1))
outputs = tf.keras.layers.TimeDistributed(cnn)(main_input)
outputs.shape

Output

TensorShape([None, 10, 2419200])