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,