Python lasagne.layers.SliceLayer() Examples

def create_network():
    l = 1000
    pool_size = 5
    test_size1 = 13
    test_size2 = 7
    test_size3 = 5
    kernel1 = 128
    kernel2 = 128
    kernel3 = 128
    layer1 = InputLayer(shape=(None, 1, 4, l+1024))
    layer2_1 = SliceLayer(layer1, indices=slice(0, l), axis = -1)
    layer2_2 = SliceLayer(layer1, indices=slice(l, None), axis = -1)
    layer2_3 = SliceLayer(layer2_2, indices = slice(0,4), axis = -2)
    layer2_f = FlattenLayer(layer2_3)
    layer3 = Conv2DLayer(layer2_1,num_filters = kernel1, filter_size = (4,test_size1))
    layer4 = Conv2DLayer(layer3,num_filters = kernel1, filter_size = (1,test_size1))
    layer5 = Conv2DLayer(layer4,num_filters = kernel1, filter_size = (1,test_size1))
    layer6 = MaxPool2DLayer(layer5, pool_size = (1,pool_size))
    layer7 = Conv2DLayer(layer6,num_filters = kernel2, filter_size = (1,test_size2))
    layer8 = Conv2DLayer(layer7,num_filters = kernel2, filter_size = (1,test_size2))
    layer9 = Conv2DLayer(layer8,num_filters = kernel2, filter_size = (1,test_size2))
    layer10 = MaxPool2DLayer(layer9, pool_size = (1,pool_size))
    layer11 = Conv2DLayer(layer10,num_filters = kernel3, filter_size = (1,test_size3))
    layer12 = Conv2DLayer(layer11,num_filters = kernel3, filter_size = (1,test_size3))
    layer13 = Conv2DLayer(layer12,num_filters = kernel3, filter_size = (1,test_size3))
    layer14 = MaxPool2DLayer(layer13, pool_size = (1,pool_size))
    layer14_d = DenseLayer(layer14, num_units= 256)
    layer3_2 = DenseLayer(layer2_f, num_units = 128)
    layer15 = ConcatLayer([layer14_d,layer3_2])
    layer16 = DropoutLayer(layer15,p=0.5)
    layer17 = DenseLayer(layer16, num_units=256)
    network = DenseLayer(layer17, num_units= 2, nonlinearity=softmax)
    return network


#random search to initialize the weights 

def create_network():
    l = 1000
    pool_size = 5
    test_size1 = 13
    test_size2 = 7
    test_size3 = 5
    kernel1 = 128
    kernel2 = 128
    kernel3 = 128
    layer1 = InputLayer(shape=(None, 1, 4, l+1024))
    layer2_1 = SliceLayer(layer1, indices=slice(0, l), axis = -1)
    layer2_2 = SliceLayer(layer1, indices=slice(l, None), axis = -1)
    layer2_3 = SliceLayer(layer2_2, indices = slice(0,4), axis = -2)
    layer2_f = FlattenLayer(layer2_3)
    layer3 = Conv2DLayer(layer2_1,num_filters = kernel1, filter_size = (4,test_size1))
    layer4 = Conv2DLayer(layer3,num_filters = kernel1, filter_size = (1,test_size1))
    layer5 = Conv2DLayer(layer4,num_filters = kernel1, filter_size = (1,test_size1))
    layer6 = MaxPool2DLayer(layer5, pool_size = (1,pool_size))
    layer7 = Conv2DLayer(layer6,num_filters = kernel2, filter_size = (1,test_size2))
    layer8 = Conv2DLayer(layer7,num_filters = kernel2, filter_size = (1,test_size2))
    layer9 = Conv2DLayer(layer8,num_filters = kernel2, filter_size = (1,test_size2))
    layer10 = MaxPool2DLayer(layer9, pool_size = (1,pool_size))
    layer11 = Conv2DLayer(layer10,num_filters = kernel3, filter_size = (1,test_size3))
    layer12 = Conv2DLayer(layer11,num_filters = kernel3, filter_size = (1,test_size3))
    layer13 = Conv2DLayer(layer12,num_filters = kernel3, filter_size = (1,test_size3))
    layer14 = MaxPool2DLayer(layer13, pool_size = (1,pool_size))
    layer14_d = DenseLayer(layer14, num_units= 256)
    layer3_2 = DenseLayer(layer2_f, num_units = 128)
    layer15 = ConcatLayer([layer14_d,layer3_2])
    #layer16 = DropoutLayer(layer15,p=0.5)
    layer17 = DenseLayer(layer15, num_units=256)
    network = DenseLayer(layer17, num_units= 1, nonlinearity=None)
    return network


#random search to initialize the weights 

def _build(self, forget_bias=5.0, grad_clip=10.0):
        """Build architecture
        """
        network = InputLayer(shape=(None, self.seq_length, self.input_size),
                             name='input')
        self.input_var = network.input_var

        # Hidden layers
        tanh = lasagne.nonlinearities.tanh
        gate, constant = lasagne.layers.Gate, lasagne.init.Constant
        for _ in range(self.depth):
            network = LSTMLayer(network, self.width, nonlinearity=tanh,
                                grad_clipping=grad_clip,
                                forgetgate=gate(b=constant(forget_bias)))

        # Retain last-output state
        network = SliceLayer(network, -1, 1)

        # Output layer
        sigmoid = lasagne.nonlinearities.sigmoid
        loc_layer = DenseLayer(network, self.num_outputs * 2)
        conf_layer = DenseLayer(network, self.num_outputs,
                                nonlinearity=sigmoid)

        # Grab all layers into DAPs instance
        self.network = get_all_layers([loc_layer, conf_layer])

        # Get theano expression for outputs of DAPs model
        self.loc_var, self.conf_var = get_output([loc_layer, conf_layer],
                                                 deterministic=True) 

def __init__(self, incoming, channel_layer_class, name=None, **channel_layer_kwargs):
        super(ChannelwiseLayer, self).__init__(incoming, name=name)
        self.channel_layer_class = channel_layer_class
        self.channel_incomings = []
        self.channel_outcomings = []
        for channel in range(lasagne.layers.get_output_shape(incoming)[0]):
            channel_incoming = L.SliceLayer(incoming, indices=slice(channel, channel+1), axis=1,
                                            name='%s.%s%d' % (name, 'slice', channel) if name is not None else None)
            channel_outcoming = channel_layer_class(channel_incoming,
                                                    name='%s.%s%d' % (name, 'op', channel) if name is not None else None,
                                                    **channel_layer_kwargs)
            self.channel_incomings.append(channel_incoming)
            self.channel_outcomings.append(channel_outcoming)
        self.outcoming = L.ConcatLayer(self.channel_outcomings, axis=1,
                                       name='%s.%s' % (name, 'concat') if name is not None else None) 

def build_convpool_lstm(input_vars, nb_classes, grad_clip=110, imsize=32, n_colors=3, n_timewin=7):
    """
    Builds the complete network with LSTM layer to integrate time from sequences of EEG images.

    :param input_vars: list of EEG images (one image per time window)
    :param nb_classes: number of classes
    :param grad_clip:  the gradient messages are clipped to the given value during
                        the backward pass.
    :param imsize: size of the input image (assumes a square input)
    :param n_colors: number of color channels in the image
    :param n_timewin: number of time windows in the snippet
    :return: a pointer to the output of last layer
    """
    convnets = []
    w_init = None
    # Build 7 parallel CNNs with shared weights
    for i in range(n_timewin):
        if i == 0:
            convnet, w_init = build_cnn(input_vars[i], imsize=imsize, n_colors=n_colors)
        else:
            convnet, _ = build_cnn(input_vars[i], w_init=w_init, imsize=imsize, n_colors=n_colors)
        convnets.append(FlattenLayer(convnet))
    # at this point convnets shape is [numTimeWin][n_samples, features]
    # we want the shape to be [n_samples, features, numTimeWin]
    convpool = ConcatLayer(convnets)
    convpool = ReshapeLayer(convpool, ([0], n_timewin, get_output_shape(convnets[0])[1]))
    # Input to LSTM should have the shape as (batch size, SEQ_LENGTH, num_features)
    convpool = LSTMLayer(convpool, num_units=128, grad_clipping=grad_clip,
        nonlinearity=lasagne.nonlinearities.tanh)
    # We only need the final prediction, we isolate that quantity and feed it
    # to the next layer.
    convpool = SliceLayer(convpool, -1, 1)      # Selecting the last prediction
    # A fully-connected layer of 256 units with 50% dropout on its inputs:
    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),
            num_units=256, nonlinearity=lasagne.nonlinearities.rectify)
    # And, finally, the output layer with 50% dropout on its inputs:
    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),
            num_units=nb_classes, nonlinearity=lasagne.nonlinearities.softmax)
    return convpool 

def util_slice_layer(self, layer, persons_cnt, factor):
        g_sz = persons_cnt//factor
        
        layers = []
        
        for i in range(factor):
            layer_i = SliceLayer(layer, indices=slice(i*g_sz, (i+1)*g_sz), axis=2)
            layers.append(layer_i)
              
        return layers     
      
    ############################################################################ 

def build_convpool_mix(input_vars, nb_classes, grad_clip=110, imsize=32, n_colors=3, n_timewin=7):
    """
    Builds the complete network with LSTM and 1D-conv layers combined

    :param input_vars: list of EEG images (one image per time window)
    :param nb_classes: number of classes
    :param grad_clip:  the gradient messages are clipped to the given value during
                        the backward pass.
    :param imsize: size of the input image (assumes a square input)
    :param n_colors: number of color channels in the image
    :param n_timewin: number of time windows in the snippet
    :return: a pointer to the output of last layer
    """
    convnets = []
    w_init = None
    # Build 7 parallel CNNs with shared weights
    for i in range(n_timewin):
        if i == 0:
            convnet, w_init = build_cnn(input_vars[i], imsize=imsize, n_colors=n_colors)
        else:
            convnet, _ = build_cnn(input_vars[i], w_init=w_init, imsize=imsize, n_colors=n_colors)
        convnets.append(FlattenLayer(convnet))
    # at this point convnets shape is [numTimeWin][n_samples, features]
    # we want the shape to be [n_samples, features, numTimeWin]
    convpool = ConcatLayer(convnets)
    convpool = ReshapeLayer(convpool, ([0], n_timewin, get_output_shape(convnets[0])[1]))
    reformConvpool = DimshuffleLayer(convpool, (0, 2, 1))
    # input to 1D convlayer should be in (batch_size, num_input_channels, input_length)
    conv_out = Conv1DLayer(reformConvpool, 64, 3)
    conv_out = FlattenLayer(conv_out)
    # Input to LSTM should have the shape as (batch size, SEQ_LENGTH, num_features)
    lstm = LSTMLayer(convpool, num_units=128, grad_clipping=grad_clip,
        nonlinearity=lasagne.nonlinearities.tanh)
    lstm_out = SliceLayer(lstm, -1, 1)
    # Merge 1D-Conv and LSTM outputs
    dense_input = ConcatLayer([conv_out, lstm_out])
    # A fully-connected layer of 256 units with 50% dropout on its inputs:
    convpool = DenseLayer(lasagne.layers.dropout(dense_input, p=.5),
            num_units=512, nonlinearity=lasagne.nonlinearities.rectify)
    # And, finally, the 10-unit output layer with 50% dropout on its inputs:
    convpool = DenseLayer(convpool,
            num_units=nb_classes, nonlinearity=lasagne.nonlinearities.softmax)
    return convpool 

def network(self):
        if self._network is not None:
            return self._network

        # Build the computational graph using a dummy input.
        import lasagne
        from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
        from lasagne.layers import ElemwiseSumLayer, NonlinearityLayer, ExpressionLayer, PadLayer, InputLayer, FlattenLayer, SliceLayer
        # from lasagne.layers import batch_norm
        from lasagne.nonlinearities import rectify

        self._network_in = InputLayer(shape=(None, self.nb_channels,) + self.image_shape, input_var=None)

        convnet_layers = [self._network_in]
        convnet_layers_preact = [self._network_in]
        layer_blueprints = list(map(str.strip, self.convnet_blueprint.split("->")))
        for i, layer_blueprint in enumerate(layer_blueprints, start=1):
            "64@3x3(valid) -> 64@3x3(full)"
            nb_filters, rest = layer_blueprint.split("@")
            filter_shape, rest = rest.split("(")
            nb_filters = int(nb_filters)
            filter_shape = tuple(map(int, filter_shape.split("x")))
            pad = rest[:-1]

            preact = ConvLayer(convnet_layers[-1], num_filters=nb_filters, filter_size=filter_shape, stride=(1, 1), nonlinearity=None, pad=pad, W=lasagne.init.HeNormal(gain='relu'))

            if i > len(layer_blueprints) // 2 and i != len(layer_blueprints):
                shortcut = convnet_layers_preact[len(layer_blueprints)-i]
                if i == len(layer_blueprints):
                    if preact.output_shape[1] != shortcut.output_shape[1]:
                        shortcut = SliceLayer(shortcut, slice(0, 1), axis=1)
                    else:
                        raise NameError("Something is wrong.")

                print("Shortcut from {} to {}".format(len(layer_blueprints)-i, i))
                preact = ElemwiseSumLayer([preact, shortcut])

            convnet_layers_preact.append(preact)

            layer = NonlinearityLayer(preact, nonlinearity=rectify)
            convnet_layers.append(layer)

        self._network = FlattenLayer(preact)
        # network = DenseLayer(l, num_units=int(np.prod(self.image_shape)),
        #                      W=lasagne.init.HeNormal(),
        #                      nonlinearity=None)

        print("Nb. of parameters in model: {}".format(lasagne.layers.count_params(self._network, trainable=True)))
        return self._network