def test_merge():
layer_1 = core.Layer()
layer_2 = core.Layer()
layer_1.set_input_shape((None,))
layer_2.set_input_shape((None,))
layer = core.Merge([layer_1, layer_2])
_runner(layer)
def test_merge(self):
layer_1 = core.Layer()
layer_2 = core.Layer()
layer = core.Merge([layer_1, layer_2])
self._runner(layer)
def mergeModel(model, textmodel):
merged = core.Merge([model, textmodel], mode='concat')
return merged
conv1_filter_size,
subsample=(conv1_stride, conv1_stride),
border_mode='valid',
input_shape=(prev_frames, image_size, image_size)))
if pool1:
conv_model.add(CONV.MaxPooling2D(pool_size=(pool1_size, pool1_size)))
conv_model.add(CORE.Activation(conv1_act))
conv_model.add(CORE.Flatten())
conv_model.add(CORE.Dense(fc1_size))
conv_model.add(CORE.Activation(fc1_act))
loc_model.add(CORE.Dense(fc1_size, input_shape=(prev_frames * 4, )))
loc_model.add(CORE.Activation(fc1_act))
#model.add(CONV.Convolution2D(conv2_filters, conv2_filter_size, conv2_filter_size, border_mode='valid'))
#model.add(CONV.MaxPooling2D(pool_size=(pool2_size, pool2_size)))
#model.add(CORE.Activation(conv2_act))
model.add(CORE.Merge([conv_model, loc_model], mode='concat'))
model.add(CORE.Dense(4, init='zero'))
model.add(CORE.Activation(fc2_act))
print 'Building bouncing MNIST generator'
from data_handler import *
bmnist = BouncingMNIST(1,
seq_len,
batch_size,
image_size,
'train/inputs',
'train/targets',
clutter_size_max=14,
acc=acc_scale,
