models_tf.py

import layers_tf as 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)