keras: Cannot stack models in Model Class API

Everyone, please be professional.

@farizrahman4u This is rude comment. This is actually an error that @fchollet fixed, #4477.

You’ve made me irritated with your comment

You irritate me with your existence.

vgg_model.output feeds into input_2

No it doesn’t. If it does, kindly comment the particular line, Sir.

Here is the original code by OP. You will still get the error, with latest version of Keras; because it is supposed to.

from keras.applications.vgg16 import VGG16
from keras.models import Model
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense, Input

img_width, img_height = 150, 150

input_1 = Input(shape=(3, img_width, img_height))

vgg16_model = VGG16(include_top=False, weights='imagenet', input_tensor=input_1)

input_2 = Input(shape=vgg16_model.output_shape[1:])
x = GlobalAveragePooling2D()(input_2)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
predict = Dense(1, activation='sigmoid')(x)
top_model = Model(input=input_2, output=predict)

# load the best saved weights
#top_model.load_weights(os.path.join(data_path, 'VGG16Classifier.hdf5'))  
# add the model on top of the convolutional base

model = Model(input=input_1, output=top_model.output)

Maybe next time ask someone less rude?

The issue is exactly what the error message says. There is no path from input_1 to top_model.output. So nothing to fix on keras side.

@farizrahman4u You’ve made me irritated with your comment so I will carefully show you the path from input_t to top_model.output.

input_1 feeds into vgg16_model, vgg_model.output feeds into input_2. Input_2 goes to x via the functional API. x goes into predict and then predict goes into top_model = Model(input=input_2, output=predict).

@farizrahman4u @fchollet

This is it for me. I am moving to tensorflow.

You irritate me with your existence.

@farizrahman4u Boo hoo buddy, get over it

Just split the model:

img_width, img_height = 150, 150
vgg16_model = VGG16(include_top=False, weights='imagenet')

input = Input(batch_shape=vgg16_model.output_shape)
x = GlobalAveragePooling2D()(input)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
predict = Dense(1, activation='sigmoid')(x)
top_model = Model(input, predict)

top_model.load_weights(os.path.join(data_path, 'VGG16Classifier.hdf5'))  

input = Input(shape=(3, img_width, img_height))
x = vgg16_model(input)
predict = top_model(x)
model = Model(input, predict)