def convert(image):
image = data.norm_min_max_tf(image)
return tf.image.convert_image_dtype(image, dtype=tf.uint8, saturate=True)
# determine the sizes
roisize = 64
EPS = 1e-12
# create placeholders for batchfeeding
im_xdim, im_ydim = scale_size, scale_size
inputs_tf = tf.placeholder(tf.float32, shape=(batch_size, im_xdim, im_ydim, 1), name='inputs_tf')
outputs_tf = tf.placeholder(tf.float32, shape=(batch_size, im_xdim, im_ydim, 1), name='outputs_tf')
spikes_tf = tf.placeholder(tf.float32, shape=(batch_size, im_xdim, im_ydim, 1), name='spikes_tf')
# inputs and targets are [batch_size, height, width, channels]
C2Pmodel = model.create_model(inputs_tf, outputs_tf, ndf, ngf, EPS, gan_weight, l1_weight, l1_sparse_weight, lr, beta1)
# reverse any processing on images so they can be written to disk or displayed to user
outputs = data.deprocess_tf(C2Pmodel.outputs)
outputs = data.norm_min_max_tf(outputs)
outputs = tf.reduce_sum(outputs, axis=[0,3], name='outputs')
# define saver
saver = tf.train.Saver(max_to_keep=1)
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
print("Variables have been initialized!")
with tf.Session() as sess:
#sess = tf.InteractiveSession()
#sess = tf.Session()
# Run the initializer
sess.run(init)