Untitled

def build_model(input_shape, num_classes):
    inputs = Input(shape=input_shape)
    x = TimeDistributed(Conv3D(32, (3, 3, 3), activation='relu'))(inputs)
    x = TimeDistributed(MaxPooling3D((2, 2, 2)))(x)
    x = TimeDistributed(Conv3D(64, (3, 3, 3), activation='relu'))(x)
    x = TimeDistributed(MaxPooling3D((2, 2, 2)))(x)
    x = TimeDistributed(Flatten())(x)
    x = LSTM(128, return_sequences=False)(x)
    x = Dense(128, activation='relu')(x)
    outputs = Dense(num_classes, activation='softmax')(x)

    model = Model(inputs, outputs)
    return model

sequence_length = 10  # example sequence length
img_shape = (16, 64, 64, 3)
input_shape = (sequence_length, *img_shape)
num_classes = 10  # example number of classes

model = build_model(input_shape, num_classes)

model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()