models.py

import layers

def baseline(x, parameters, nodropout_probability = None, Gaussian_noise_std = None):
    
	if Gaussian_noise_std is not None:
		x = layers.all_views_Gaussian_noise_layer(x, Gaussian_noise_std)

	# first conv sequence
	h = layers.all_views_conv_layer(x, 'conv1', number_of_filters = 32, filter_size = [3, 3], stride = [2, 2])

	# second conv sequence
	h = layers.all_views_max_pool(h, stride = [3, 3])
	h = layers.all_views_conv_layer(h, 'conv2a', number_of_filters = 64, filter_size = [3, 3], stride=[2, 2])
	h = layers.all_views_conv_layer(h, 'conv2b', number_of_filters = 64, filter_size = [3, 3], stride=[1, 1])
	h = layers.all_views_conv_layer(h, 'conv2c', number_of_filters = 64, filter_size = [3, 3], stride=[1, 1])

	# third conv sequence
	next_sequence = True
    
	h = layers.all_views_max_pool(h, stride = [2, 2])
	h = layers.all_views_conv_layer(h, 'conv3a', number_of_filters = 128, filter_size = [3, 3], stride = [1, 1])
	h = layers.all_views_conv_layer(h, 'conv3b', number_of_filters = 128, filter_size = [3, 3], stride = [1, 1])
	h = layers.all_views_conv_layer(h, 'conv3c', number_of_filters = 128, filter_size = [3, 3], stride = [1, 1])

	# fourth conv sequence
	next_sequence = True

	h = layers.all_views_max_pool(h, stride = [2, 2])
	h = layers.all_views_conv_layer(h, 'conv4a', number_of_filters = 128, filter_size = [3, 3], stride = [1, 1])
	h = layers.all_views_conv_layer(h, 'conv4b', number_of_filters = 128, filter_size = [3, 3], stride = [1, 1])
	h = layers.all_views_conv_layer(h, 'conv4c', number_of_filters = 128, filter_size = [3, 3], stride = [1, 1])

	# fifth conv sequence
	next_sequence = True

	h = layers.all_views_max_pool(h, stride = [2, 2])
	h = layers.all_views_conv_layer(h, 'conv5a', number_of_filters = 256, filter_size = [3, 3], stride = [1, 1])
	h = layers.all_views_conv_layer(h, 'conv5b', number_of_filters = 256, filter_size = [3, 3], stride = [1, 1])
	h = layers.all_views_conv_layer(h, 'conv5c', number_of_filters = 256, filter_size = [3, 3], stride = [1, 1])

	h = layers.all_views_global_avg_pool(h)
	h = layers.all_views_flattening_layer(h)
	h = layers.fc_layer(h, number_of_units = 4 * 256)
	h = layers.dropout_layer(h, nodropout_probability)

	y_prediction_density = layers.softmax_layer(h, number_of_outputs = 4)

	return y_prediction_density

class BaselineBreastModel:

	def __init__(self, parameters, x, nodropout_probability = None, Gaussian_noise_std = None):

		self.y_prediction_density = baseline(x, parameters, nodropout_probability, Gaussian_noise_std)
        
def baseline_histogram_density(x, parameters):

	h = layers.fc_layer(x, number_of_units = 100)

	y_prediction_density = layers.softmax_layer(h, number_of_outputs = 4)
    
	return y_prediction_density

class BaselineHistogramModel:

	def __init__(self, parameters, x, nodropout_probability = None, Gaussian_noise_std = None):

		self.y_prediction_density = baseline_histogram_density(x, parameters)