我们从Python开源项目中，提取了以下18个代码示例，用于说明如何使用lasagne.layers.reshape()。
def build_net(nz=10): # nz = size of latent code #N.B. using batch_norm applies bn before non-linearity! F=32 enc = InputLayer(shape=(None,1,28,28)) enc = Conv2DLayer(incoming=enc, num_filters=F*2, filter_size=5,stride=2, nonlinearity=lrelu(0.2),pad=2) enc = Conv2DLayer(incoming=enc, num_filters=F*4, filter_size=5,stride=2, nonlinearity=lrelu(0.2),pad=2) enc = Conv2DLayer(incoming=enc, num_filters=F*4, filter_size=5,stride=1, nonlinearity=lrelu(0.2),pad=2) enc = reshape(incoming=enc, shape=(-1,F*4*7*7)) enc = DenseLayer(incoming=enc, num_units=nz, nonlinearity=sigmoid) #Generator networks dec = InputLayer(shape=(None,nz)) dec = DenseLayer(incoming=dec, num_units=F*4*7*7) dec = reshape(incoming=dec, shape=(-1,F*4,7,7)) dec = Deconv2DLayer(incoming=dec, num_filters=F*4, filter_size=4, stride=2, nonlinearity=relu, crop=1) dec = Deconv2DLayer(incoming=dec, num_filters=F*4, filter_size=4, stride=2, nonlinearity=relu, crop=1) dec = Deconv2DLayer(incoming=dec, num_filters=1, filter_size=3, stride=1, nonlinearity=sigmoid, crop=1) return enc, dec
def get_output_for(self, input, **kwargs): n_batches = input.shape[0] n_steps = input.shape[1] input = TT.reshape(input, (n_batches, n_steps, -1)) h0s = TT.tile(TT.reshape(self.h0, (1, self.num_units)), (n_batches, 1)) # flatten extra dimensions shuffled_input = input.dimshuffle(1, 0, 2) hs, _ = theano.scan(fn=self.step, sequences=[shuffled_input], outputs_info=h0s) shuffled_hs = hs.dimshuffle(1, 0, 2) return shuffled_hs
def get_output_for(self, inputs, **kwargs): x, hprev = inputs n_batch = x.shape[0] x = x.reshape((n_batch, -1)) return self._gru_layer.step(x, hprev)
def load_data(opts): dataDir = opts.inDir #/data/datasets/MNIST/mnist.pkl train,test,val = np.load(dataDir,mmap_mode='r') return train[0].reshape(-1,1,28,28).astype(floatX), train[1], test[0].reshape(-1,1,28,28).astype(floatX), test[1], val[0].astype(floatX), val[1]
def __init__(self, input_shape, output_dim, hidden_dim, hidden_nonlinearity=LN.rectify, output_nonlinearity=None, name=None, input_var=None, input_layer=None): if input_layer is None: l_in = L.InputLayer(shape=(None, None) + input_shape, input_var=input_var, name="input") else: l_in = input_layer l_step_input = L.InputLayer(shape=(None,) + input_shape) l_step_prev_hidden = L.InputLayer(shape=(None, hidden_dim)) l_gru = GRULayer(l_in, num_units=hidden_dim, hidden_nonlinearity=hidden_nonlinearity, hidden_init_trainable=False) l_gru_flat = L.ReshapeLayer( l_gru, shape=(-1, hidden_dim) ) l_output_flat = L.DenseLayer( l_gru_flat, num_units=output_dim, nonlinearity=output_nonlinearity, ) l_output = OpLayer( l_output_flat, op=lambda flat_output, l_input: flat_output.reshape((l_input.shape[0], l_input.shape[1], -1)), shape_op=lambda flat_output_shape, l_input_shape: (l_input_shape[0], l_input_shape[1], flat_output_shape[-1]), extras=[l_in] ) l_step_hidden = l_gru.get_step_layer(l_step_input, l_step_prev_hidden) l_step_output = L.DenseLayer( l_step_hidden, num_units=output_dim, nonlinearity=output_nonlinearity, W=l_output_flat.W, b=l_output_flat.b, ) self._l_in = l_in self._hid_init_param = l_gru.h0 self._l_gru = l_gru self._l_out = l_output self._l_step_input = l_step_input self._l_step_prev_hidden = l_step_prev_hidden self._l_step_hidden = l_step_hidden self._l_step_output = l_step_output
def __init__(self, input_shape, output_dim, hidden_sizes, conv_filters, conv_filter_sizes, conv_strides, conv_pads, hidden_W_init=LI.GlorotUniform(), hidden_b_init=LI.Constant(0.), output_W_init=LI.GlorotUniform(), output_b_init=LI.Constant(0.), # conv_W_init=LI.GlorotUniform(), conv_b_init=LI.Constant(0.), hidden_nonlinearity=LN.rectify, output_nonlinearity=LN.softmax, name=None, input_var=None): if name is None: prefix = "" else: prefix = name + "_" if len(input_shape) == 3: l_in = L.InputLayer(shape=(None, np.prod(input_shape)), input_var=input_var) l_hid = L.reshape(l_in, ([0],) + input_shape) elif len(input_shape) == 2: l_in = L.InputLayer(shape=(None, np.prod(input_shape)), input_var=input_var) input_shape = (1,) + input_shape l_hid = L.reshape(l_in, ([0],) + input_shape) else: l_in = L.InputLayer(shape=(None,) + input_shape, input_var=input_var) l_hid = l_in for idx, conv_filter, filter_size, stride, pad in zip( range(len(conv_filters)), conv_filters, conv_filter_sizes, conv_strides, conv_pads, ): l_hid = L.Conv2DLayer( l_hid, num_filters=conv_filter, filter_size=filter_size, stride=(stride, stride), pad=pad, nonlinearity=hidden_nonlinearity, name="%sconv_hidden_%d" % (prefix, idx), convolution=wrapped_conv, ) for idx, hidden_size in enumerate(hidden_sizes): l_hid = L.DenseLayer( l_hid, num_units=hidden_size, nonlinearity=hidden_nonlinearity, name="%shidden_%d" % (prefix, idx), W=hidden_W_init, b=hidden_b_init, ) l_out = L.DenseLayer( l_hid, num_units=output_dim, nonlinearity=output_nonlinearity, name="%soutput" % (prefix,), W=output_W_init, b=output_b_init, ) self._l_in = l_in self._l_out = l_out self._input_var = l_in.input_var
