Python lasagne.nonlinearities.sigmoid() Examples

The following are 30 code examples of lasagne.nonlinearities.sigmoid(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module lasagne.nonlinearities , or try the search function .
Example #1
Source File: conv_sup_cc_mllsll.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network_mll = layers.DenseLayer(incoming = hidden_layer, num_units = 12, nonlinearity = sigmoid);
    network_sll = layers.DenseLayer(incoming = hidden_layer, num_units = 7, nonlinearity = sigmoid);
    network = lasagne.layers.ConcatLayer([network_mll, network_sll], axis = 1);

    return network, encode_layer, input_var, aug_var, target_var;
Example #2
Source File: conv_sup_cc_lbp.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn, fea_len):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    fea_var = T.matrix('fea_var');
    target_var = T.imatrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);
    fea_layer = layers.InputLayer(shape=(None, fea_len), input_var = fea_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer, fea_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, fea_var, target_var;
Example #3
Source File: conv_sup_cc.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.imatrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #4
Source File: conv_sup_cc_lbp.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn, fea_len):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    fea_var = T.matrix('fea_var');
    target_var = T.imatrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);
    fea_layer = layers.InputLayer(shape=(None, fea_len), input_var = fea_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer, fea_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, fea_var, target_var;
Example #5
Source File: conv_sup_cc.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #6
Source File: conv_sup_cc.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #7
Source File: conv_sup_cc_4ch.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model_4ch/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 4, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #8
Source File: conv_sup_cc_lbp.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn, fea_len):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    fea_var = T.matrix('fea_var');
    target_var = T.imatrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);
    fea_layer = layers.InputLayer(shape=(None, fea_len), input_var = fea_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer, fea_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, fea_var, target_var;
Example #9
Source File: conv_sup_cc.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #10
Source File: conv_sup_cc_4ch_rot.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model_4ch_rot/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 4, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #11
Source File: conv_sup_cc_mllsll.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network_mll = layers.DenseLayer(incoming = hidden_layer, num_units = 12, nonlinearity = sigmoid);
    network_sll = layers.DenseLayer(incoming = hidden_layer, num_units = 7, nonlinearity = sigmoid);
    network = lasagne.layers.ConcatLayer([network_mll, network_sll], axis = 1);

    return network, encode_layer, input_var, aug_var, target_var;
Example #12
Source File: conv_sup_cc_lbp.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn, fea_len):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    fea_var = T.matrix('fea_var');
    target_var = T.imatrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);
    fea_layer = layers.InputLayer(shape=(None, fea_len), input_var = fea_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer, fea_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, fea_var, target_var;
Example #13
Source File: lsgan_cifar10.py    From Theano-MPI with Educational Community License v2.0 6 votes vote down vote up 
def build_critic(input_var=None, verbose=False):
    from lasagne.layers import (InputLayer, Conv2DLayer, ReshapeLayer,
                                DenseLayer)
    try:
        from lasagne.layers.dnn import batch_norm_dnn as batch_norm
    except ImportError:
        from lasagne.layers import batch_norm
    from lasagne.nonlinearities import LeakyRectify, sigmoid
    lrelu = LeakyRectify(0.2)
    # input: (None, 1, 28, 28)
    layer = InputLayer(shape=(None, 3, 32, 32), input_var=input_var)
    # two convolutions
    layer = batch_norm(Conv2DLayer(layer, 128, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    layer = batch_norm(Conv2DLayer(layer, 256, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    layer = batch_norm(Conv2DLayer(layer, 512, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    # # fully-connected layer
    # layer = batch_norm(DenseLayer(layer, 1024, nonlinearity=lrelu))
    # output layer (linear)
    layer = DenseLayer(layer, 1, nonlinearity=None)
    if verbose: print ("critic output:", layer.output_shape)
    return layer
Example #14
Source File: conv_sup_cc_lbp.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn, fea_len):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    fea_var = T.matrix('fea_var');
    target_var = T.imatrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);
    fea_layer = layers.InputLayer(shape=(None, fea_len), input_var = fea_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer, fea_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, fea_var, target_var;
Example #15
Source File: conv_sup_cc_lbp.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn, fea_len):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    fea_var = T.matrix('fea_var');
    target_var = T.imatrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);
    fea_layer = layers.InputLayer(shape=(None, fea_len), input_var = fea_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer, fea_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, fea_var, target_var;
Example #16
Source File: conv_sup_cc.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #17
Source File: conv_sup_cc.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #18
Source File: conv_sup_cc_lbp.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn, fea_len):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    fea_var = T.matrix('fea_var');
    target_var = T.imatrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);
    fea_layer = layers.InputLayer(shape=(None, fea_len), input_var = fea_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer, fea_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, fea_var, target_var;
Example #19
Source File: conv_sup_cc_4ch_rot.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model_4ch_rot/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 4, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #20
Source File: conv_sup_cc.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #21
Source File: conv_sup_cc_lbp.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn, fea_len):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    fea_var = T.matrix('fea_var');
    target_var = T.imatrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);
    fea_layer = layers.InputLayer(shape=(None, fea_len), input_var = fea_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer, fea_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, fea_var, target_var;
Example #22
Source File: conv_sup_cc_mllsll.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network_mll = layers.DenseLayer(incoming = hidden_layer, num_units = 12, nonlinearity = sigmoid);
    network_sll = layers.DenseLayer(incoming = hidden_layer, num_units = 7, nonlinearity = sigmoid);
    network = lasagne.layers.ConcatLayer([network_mll, network_sll], axis = 1);

    return network, encode_layer, input_var, aug_var, target_var;
Example #23
Source File: conv_sup_cc_4ch.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model_4ch/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 4, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #24
Source File: conv_sup_cc.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #25
Source File: models_uncond.py    From EvolutionaryGAN with MIT License 6 votes vote down vote up 
def build_discriminator_toy(image=None, nd=512, GP_norm=None):
    Input = InputLayer(shape=(None, 2), input_var=image)
    print ("Dis input:", Input.output_shape)
    dis0 = DenseLayer(Input, nd, W=Normal(0.02), nonlinearity=relu)
    print ("Dis fc0:", dis0.output_shape)
    if GP_norm is True:
        dis1 = DenseLayer(dis0, nd, W=Normal(0.02), nonlinearity=relu)
    else:
        dis1 = batch_norm(DenseLayer(dis0, nd, W=Normal(0.02), nonlinearity=relu))
    print ("Dis fc1:", dis1.output_shape)
    if GP_norm is True:
        dis2 = batch_norm(DenseLayer(dis1, nd, W=Normal(0.02), nonlinearity=relu))
    else:
        dis2 = DenseLayer(dis1, nd, W=Normal(0.02), nonlinearity=relu)
    print ("Dis fc2:", dis2.output_shape)
    disout = DenseLayer(dis2, 1, W=Normal(0.02), nonlinearity=sigmoid)
    print ("Dis output:", disout.output_shape)
    return disout
Example #26
Source File: conv_sup_cc_mllsll.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network_mll = layers.DenseLayer(incoming = hidden_layer, num_units = 12, nonlinearity = sigmoid);
    network_sll = layers.DenseLayer(incoming = hidden_layer, num_units = 7, nonlinearity = sigmoid);
    network = lasagne.layers.ConcatLayer([network_mll, network_sll], axis = 1);

    return network, encode_layer, input_var, aug_var, target_var;
Example #27
Source File: conv_sup_cc_4ch.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model_4ch/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 4, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #28
Source File: conv_sup_cc.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;
Example #29
Source File: conv_sup_cc_mllsll.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 3, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network_mll = layers.DenseLayer(incoming = hidden_layer, num_units = 12, nonlinearity = sigmoid);
    network_sll = layers.DenseLayer(incoming = hidden_layer, num_units = 7, nonlinearity = sigmoid);
    network = lasagne.layers.ConcatLayer([network_mll, network_sll], axis = 1);

    return network, encode_layer, input_var, aug_var, target_var;
Example #30
Source File: conv_sup_cc_4ch_rot.py    From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def build_network_from_ae(classn):
    input_var = T.tensor4('inputs');
    aug_var = T.matrix('aug_var');
    target_var = T.matrix('targets');

    ae = pickle.load(open('model_4ch_rot/conv_ae.pkl', 'rb'));

    input_layer_index = map(lambda pair : pair[0], ae.layers).index('input');
    first_layer = ae.get_all_layers()[input_layer_index + 1];
    input_layer = layers.InputLayer(shape=(None, 4, 32, 32), input_var = input_var);
    first_layer.input_layer = input_layer;

    encode_layer_index = map(lambda pair : pair[0], ae.layers).index('encode_layer');
    encode_layer = ae.get_all_layers()[encode_layer_index];
    aug_layer = layers.InputLayer(shape=(None, classn), input_var = aug_var);

    cat_layer = lasagne.layers.ConcatLayer([encode_layer, aug_layer], axis = 1);
    hidden_layer = layers.DenseLayer(incoming = cat_layer, num_units = 100, nonlinearity = rectify);

    network = layers.DenseLayer(incoming = hidden_layer, num_units = classn, nonlinearity = sigmoid);

    return network, encode_layer, input_var, aug_var, target_var;