Working Deconv2D example for Keras 1.1.2 and TensorFlow 0.11-0.12

keras_deconvolution2d_example.py

import os
os.environ["KERAS_BACKEND"] = "tensorflow"

import numpy as np

from keras.models import Sequential
from keras.layers import Deconvolution2D

import warnings

# apply a 3x3 transposed convolution with stride 1x1 and 3 output filters on a 12x12 image:
model = Sequential()
model.add(Deconvolution2D(3, 3, 3, output_shape=(32, 14, 14, 3), border_mode='valid', input_shape=(12, 12, 3)))
model.summary()
# Note that you will have to change the output_shape depending on the backend used.
# we can predict with the model and print the shape of the array.
dummy_input = np.ones((32, 12, 12, 3))
# For TensorFlow dummy_input = np.ones((32, 12, 12, 3))
preds = model.predict(dummy_input)
print(preds.shape)
# Theano GPU: (None, 3, 13, 13)
# Theano CPU: (None, 3, 14, 14)
# TensorFlow: (None, 14, 14, 3)

model = Sequential()
model.add(Deconvolution2D(3, 3, 3, output_shape=(32, 25, 25, 3), subsample=(2, 2), border_mode='valid', input_shape=(12, 12, 3)))
model.summary()

# we can predict with the model and print the shape of the array.
dummy_input = np.ones((32, 12, 12, 3))
# For TensorFlow dummy_input = np.ones((32, 12, 12, 3))
preds = model.predict(dummy_input)
print(preds.shape)
# Theano GPU: (None, 3, 25, 25)
# Theano CPU: (None, 3, 25, 25)
# TensorFlow: (None, 25, 25, 3)