def __init__( self, input, input_shape, id, rng=None, input_params=None, dropout_rate=0, verbose=2, ): if verbose >= 3: print "... setting up the dropout layer, just in case." if rng is None: rng = numpy.random super(dropout_batch_norm_layer_1d, self).__init__(input=input, input_shape=input_shape, id=id, rng=rng, borrow=borrow, input_params=input_params, verbose=verbose) if not dropout_rate == 0: self.output = _dropout(rng=rng, params=self.output, dropout_rate=dropout_rate) if verbose >= 3: print "... Dropped out" self.dropout_rate = dropout_rate
def __init__(self, input, num_neurons, input_shape, id, dropout_rate=0.5, rng=None, input_params=None, borrow=True, activation='relu', batch_norm=True, verbose=2): if verbose >= 3: print "... set up the dropout dot product layer" if rng is None: rng = numpy.random super(dropout_dot_product_layer, self).__init__(input=input, num_neurons=num_neurons, input_shape=input_shape, id=id, rng=rng, input_params=input_params, borrow=borrow, activation=activation, batch_norm=batch_norm, verbose=verbose) if not dropout_rate == 0: self.output = _dropout(rng=rng, params=self.output, dropout_rate=dropout_rate) self.dropout_rate = dropout_rate if verbose >= 3: print "... Dropped out"
def __init__(self, input, input_shape, id, rng=None, dropout_rate=0.5, angle=None, borrow=True, verbose=2): if verbose >= 3: print("... set up the dropout rotate layer") if rng is None: rng = numpy.random super(dropout_rotate_layer, self).__init__(input=input, input_shape=input_shape, id=id, borrow=borrow, verbose=verbose) if not dropout_rate == 0: self.output = _dropout(rng=rng, params=self.output, dropout_rate=dropout_rate) self.dropout_rate = dropout_rate if verbose >= 3: print("... Dropped out")
def __init__(self, x, input_shape, id=-1, error='rmse', rng=None, dropout_rate=0.5, verbose=2): if verbose >= 3: print "... set up the dropout merge layer" if rng is None: rng = numpy.random super(dropout_merge_layer, self).__init__(x=x, input_shape=input_shape, error=error, id=id, verbose=verbose) if not dropout_rate == 0: self.output = _dropout(rng=rng, params=self.output, dropout_rate=dropout_rate) if verbose >= 3: print "... Dropped out"
def __init__( self, input, nkerns, input_shape, id, output_shape, dropout_rate=0.5, filter_shape=(3, 3), poolsize=(1, 1), pooltype='max', batch_norm=True, border_mode='valid', stride=(1, 1), rng=None, borrow=True, activation='relu', input_params=None, verbose=2, ): if verbose >= 3: print "... setting up the dropout layer, just in case." if rng is None: rng = numpy.random super(dropout_deconv_layer_2d, self).__init__(input=input, nkerns=nkerns, input_shape=input_shape, id=id, output_shape=output_shape, filter_shape=filter_shape, poolsize=poolsize, pooltype=pooltype, batch_norm=batch_norm, border_mode=border_mode, stride=stride, rng=rng, borrow=borrow, activation=activation, input_params=input_params, verbose=verbose) if not dropout_rate == 0: self.output = _dropout(rng=rng, params=self.output, dropout_rate=dropout_rate) if verbose >= 3: print "... Dropped out" self.dropout_rate = dropout_rate
def __init__( self, input, nkerns, input_shape, id, dropout_rate = 0.5, filter_shape = (3,3), poolsize = (2,2), pooltype = 'max', batch_norm = True, border_mode = 'valid', stride = (1,1), rng = None, borrow = True, activation = 'relu', input_params = None, verbose = 2, ): if verbose >=3: print "... setting up the dropout layer, just in case." if rng is None: rng = numpy.random super(dropout_conv_pool_layer_2d, self).__init__( input = input, nkerns = nkerns, input_shape = input_shape, id = id, filter_shape = filter_shape, poolsize = poolsize, pooltype = pooltype, batch_norm = batch_norm, border_mode = border_mode, stride = stride, rng = rng, borrow = borrow, activation = activation, input_params = input_params, verbose = verbose ) if not dropout_rate == 0: self.output = _dropout(rng = rng, params = self.output, dropout_rate = dropout_rate) if verbose >=3: print "... Dropped out" self.dropout_rate = dropout_rate
def __init__ (self, input, num_neurons, input_shape, id, dropout_rate = 0.5, rng = None, input_params = None, borrow = True, activation = 'relu', batch_norm = True, verbose = 2 ): if verbose >= 3: print "... set up the dropout dot product layer" if rng is None: rng = numpy.random super(dropout_dot_product_layer, self).__init__ ( input = input, num_neurons = num_neurons, input_shape = input_shape, id = id, rng = rng, input_params = input_params, borrow = borrow, activation = activation, batch_norm = batch_norm, verbose = verbose ) if not dropout_rate == 0: self.output = _dropout(rng = rng, params = self.output, dropout_rate = dropout_rate) self.dropout_rate = dropout_rate if verbose >=3: print "... Dropped out"