def getLayerIndexByName(model, layername):
    for idx, layer in enumerate(model.layers):
        if layer.name == layername:
            return idx

idx = getLayerIndexByName(resnet, 'res3a_branch2a')

input_shape = resnet.layers[idx].get_input_shape_at(0) # which is here in my case (None, 55, 55, 256)

layer_input = Input(shape=input_shape[1:]) # as keras will add the batch shape

# create the new nodes for each layer in the path
x = layer_input
for layer in resnet.layers[idx:]:
    x = layer(x)

# create the model
new_model = Model(layer_input, x)

ValueError: Input 0 is incompatible with layer res3a_branch1: expected axis -1 of input shape to have value 256 but got shape (None, 28, 28, 512).

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

ValueError: Unknown layer: Scale

import resnet   # pip install resnet
from keras.models import Model
from keras.layers import Input

def getLayerIndexByName(model, layername):
    for idx, layer in enumerate(model.layers):
        if layer.name == layername:
            return idx


resnet = resnet.ResNet152(weights='imagenet')

idx = getLayerIndexByName(resnet, 'res3a_branch2a')

model1 = Model(inputs=resnet.input, outputs=resnet.get_layer('res3a_branch2a').output)

input_shape = resnet.layers[idx].get_input_shape_at(0) # get the input shape of desired layer
print(input_shape[1:])
layer_input = Input(shape=input_shape[1:]) # a new input tensor to be able to feed the desired layer

# create the new nodes for each layer in the path
x = layer_input
for layer in resnet.layers[idx:]:
    x = layer(x)

# create the model
model2 = Model(layer_input, x)

model2.summary()

ValueError: Input 0 is incompatible with layer res3a_branch1: expected axis -1 of input shape to have value 256 but got shape (None, 28, 28, 512)

from keras.applications.resnet50 import ResNet50
from keras import models
from keras import layers

resnet = ResNet50()

# this is the split point, i.e. the starting layer in our sub-model
starting_layer_name = 'activation_46'

# create a new input layer for our sub-model we want to construct
new_input = layers.Input(batch_shape=resnet.get_layer(starting_layer_name).get_input_shape_at(0))

layer_outputs = {}
def get_output_of_layer(layer):
    print(layer.name)
    # if we have already applied this layer in its input(s),
    # just return the output
    if layer.name in layer_outputs:
        return layer_outputs[layer.name]

    # if this is the starting layer, then apply it on the input tensor
    if layer.name == starting_layer_name:
        out = layer(new_input)
        layer_outputs[layer.name] = out
        return out

    # find all the connected layers which this layer
    # consumes their output
    prev_layers = []
    for node in layer._inbound_nodes:
        prev_layers.extend(node.inbound_layers)

    # get the output of connected layers
    pl_outs = []
    for pl in prev_layers:
        pl_outs.extend([get_output_of_layer(pl)])

    # apply this layer on the collected outputs
    out = layer(pl_outs[0] if len(pl_outs) == 1 else pl_outs)
    layer_outputs[layer.name] = out
    return out

# note that we start from the last layer of our desired sub-model.
# this layer could be any layer of the original model as long as it is
# reachable from the starting layer
new_output = get_output_of_layer(resnet.layers[-1])

# create the sub-model
model = models.Model(new_input, new_output)

from keras.applications.resnet50 import ResNet50
from keras.utils import plot_model

resnet = ResNet50()
plot_model(model, to_file='resnet_model.png')