Feeding input to intermediate layer fails with Graph disconnected Exception

I am writing a pipeline that fine-tunes the pre-trained models of Keras 1.2.0. To speed it up, instead of freezing the layers I try to:

1. Feed the training images once to the “frozen” part of the network and store the intermediate output to a file.
2. Train iteratively the remaining network by feeding directly the intermediate output from the file.

If you don’t use data augmentation, this should yield a significant speed improvement. Unfortunately the step 2 fails with a “Graph Disconnected” exception. I tried alternative ways to do this (such as using the K.function() approach) but it still fails.

Below you will find a simple example that reproduces the problem and the error message:

import keras.applications
from keras.models import Model
from keras.layers import Input
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
import numpy as np

# Read some random image
img = image.load_img('/path/to/image.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

# Load a pre-trained model
model = keras.applications.resnet50.ResNet50(weights='imagenet', include_top=False, input_tensor=Input(shape=(224, 224, 3)))

# Feed the image and get the bn_conv1 output: WORKS!
bn_conv1_model = Model(input=model.input, output=model.get_layer('bn_conv1').output)
bn_conv1_output = bn_conv1_model.predict(x)

# Feed directly the bn_conv1 output to the remaining layers: FAILS!
avg_pool_model = Model(input=Input(model.get_layer('bn_conv1').output_shape[1:]), output=model.get_layer('avg_pool').output) # This line throws exception
avg_pool_output = avg_pool_model.predict(bn_conv1_output)

The error message is: Traceback (most recent call last): File “<stdin>”, line 1, in <module> File “/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py”, line 1987, in init str(layers_with_complete_input)) RuntimeError: Graph disconnected: cannot obtain value for tensor Tensor(“input_1:0”, shape=(?, 224, 224, 3), dtype=float32) at layer “input_1”. The following previous layers were accessed without issue: []

[bstriner]

Graph disconnected normally means your input and output are not part of the same graph. If your input was not the variable you used to create your output, this is the error you will get.

[datumbox]

Hi @engharat

I have not yet written a proper solution for that. The network graphs can be very complex, especially for networks that branch-out and merge a lot and this requires writting graph traversal algorithms. On the future I’ll probably do this and contribute it back to Keras but I have not done it yet.

Below I send you the latest version of the “terrible” solution that I’m using. Rest assured it is equally terrible as the previous one:

def split(model, start, end):
    confs = model.get_config()
    kept_layers = set()
    for i, l in enumerate(confs['layers']):
        if i == 0:
            confs['layers'][0]['config']['batch_input_shape'] = model.layers[start].input_shape
            if i != start:
                confs['layers'][0]['name'] += str(random.randint(0, 100000000)) # rename the input layer to avoid conflicts on merge
                confs['layers'][0]['config']['name'] = confs['layers'][0]['name']
        elif i < start or i > end:
            continue
        kept_layers.add(l['name'])
    # filter layers
    layers = [l for l in confs['layers'] if l['name'] in kept_layers]
    layers[1]['inbound_nodes'][0][0][0] = layers[0]['name']
    # set conf
    confs['layers'] = layers
    confs['input_layers'][0][0] = layers[0]['name']
    confs['output_layers'][0][0] = layers[-1]['name']
    # create new model
    submodel = Model.from_config(confs)
    for l in submodel.layers:
        orig_l = model.get_layer(l.name)
        if orig_l is not None:
            l.set_weights(orig_l.get_weights())
    return submodel