ValueError after converting to python 3, keras vesion 2.1.4

[Timmarh]

I am trying to convert the build.py to python 3 and keras version 2.1.4. I came a long way but I have some trouble with (hopefully) the last step. Below I posted the code I have now but what I need to do yet is the final reshaping. I receive the error ValueError: total size of new array must be unchanged which according to this answer I got this error because the shape of my output is not corresponding to what I want to reshape it to. However my height and width are both factors of 32 so I do not understand the problem. The final layer of the network (a batch normalization) returns a shape of (Bat (None, 0, 256, 2). According to the code I found I have to reshape this using

autoencoder.add(Reshape((n_labels, img_h*img_w))

which would be (2, (256 * 256)). This sounds incorrect the 256 * 256 part is very large, but I don't fully understand the reshape function.

I saw similair questions in other threads but there the problem seems to be that tensorflow was used instead of theano. I am using Theano as backend.

Can somebody shed some light on what this reshaping does and maybe suggest to what dimensions I need to reshape?

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This is the entire model I'm using:

from keras import models
from keras.layers.core import Activation, Reshape, Permute
from keras.layers.convolutional import Conv2D, MaxPooling2D, UpSampling2D
from keras.layers.normalization import BatchNormalization
import json

img_w = 256
img_h = 256
n_labels = 2

kernel = 3

encoding_layers = [
    Conv2D(64, (kernel,kernel), padding='same', input_shape=(1, img_h, img_w)),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(64, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    MaxPooling2D(),

    Conv2D(128, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(128, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    MaxPooling2D(),

    Conv2D(256, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(256, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(256, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    MaxPooling2D(),

    Conv2D(512, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(512, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(512, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    MaxPooling2D(),

    Conv2D(512, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(512, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(512, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    MaxPooling2D(),
]

autoencoder = models.Sequential()
autoencoder.encoding_layers = encoding_layers

for l in autoencoder.encoding_layers:
    autoencoder.add(l)

decoding_layers = [
    UpSampling2D(),
    Conv2D(512, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(512, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(512, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),

    UpSampling2D(),
    Conv2D(512, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(512, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(256, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),

    UpSampling2D(),
    Conv2D(256, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(256, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(128, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),

    UpSampling2D(),
    Conv2D(128, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(64, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),

    UpSampling2D(),
    Conv2D(64, (kernel,kernel), padding='same'),
    BatchNormalization(),
    Activation('relu'),
    Conv2D(n_labels, (1, 1), padding='valid'),
    BatchNormalization(),
]
autoencoder.decoding_layers = decoding_layers
for l in autoencoder.decoding_layers:
    autoencoder.add(l)

autoencoder.add(Reshape((n_labels, img_h * img_w)))
autoencoder.add(Permute((2, 1)))
autoencoder.add(Activation('softmax'))

with open('model_5l.json', 'w') as outfile:
    outfile.write(json.dumps(json.loads(autoencoder.to_json()), indent=2))

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