I wanted to merge two sequential models into one using a Merge layer but it is showing me an error. I am working with images, with size 128x128 (RGB image) and batch size is 32.
The error is:
ValueError: The model expects 3 input arrays, but only received one array. Found: array with shape (32, 3, 128, 128)
The model is defined as:
model = Sequential() leftBranch = Sequential()
leftBranch.add(Reshape((3,128,128), input_shape=(3, img_width, img_height)))
leftBranch.add(Convolution2D(14, 3, 1, activation='relu'))
leftBranch.add(ZeroPadding2D((1, 1)))
leftBranch.add(Flatten())
rightBranch = Sequential()
rightBranch.add(Reshape((3,128,128), input_shape=(3, img_width, img_height)))
rightBranch.add(Convolution2D(14, 1, 3, activation='relu'))
rightBranch.add(MaxPooling2D((2, 2), strides=(2, 2)))
rightBranch.add(Flatten())
centralBranch = Sequential()
centralBranch.add(Reshape((3,128,128), input_shape=(3, img_width, img_height)))
centralBranch.add(Convolution2D(14, 5, 5, activation='relu'))
centralBranch.add(MaxPooling2D((2, 2), strides=(2, 2)))
centralBranch.add(Flatten())
merged = Merge([leftBranch, centralBranch, rightBranch], mode='concat')
model = Sequential()
model.add(merged) model.add(Dense(64))
model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(1))
model.add(Activation('sigmoid'))
Error coming is :
ValueError: The model expects 3 input arrays, but only received one array. Found: array with shape (32, 3, 128, 128)
So, whats is the proper way to concatenate two sequential model with convolutional layers. I just want to Merge convolutional layers output like I did it here.
