Python tensor.set_subtensor方法代码示例

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def ctc_update_log_p(skip_idxs, zeros, active, log_p_curr, log_p_prev):
    active_skip_idxs = skip_idxs[(skip_idxs < active).nonzero()]
    active_next = T.cast(T.minimum(
        T.maximum(
            active + 1,
            T.max(T.concatenate([active_skip_idxs, [-1]])) + 2 + 1
        ), log_p_curr.shape[0]), 'int32')

    common_factor = T.max(log_p_prev[:active])
    p_prev = T.exp(log_p_prev[:active] - common_factor)
    _p_prev = zeros[:active_next]
    # copy over
    _p_prev = T.set_subtensor(_p_prev[:active], p_prev)
    # previous transitions
    _p_prev = T.inc_subtensor(_p_prev[1:], _p_prev[:-1])
    # skip transitions
    _p_prev = T.inc_subtensor(_p_prev[active_skip_idxs + 2], p_prev[active_skip_idxs])
    updated_log_p_prev = T.log(_p_prev) + common_factor

    log_p_next = T.set_subtensor(
        zeros[:active_next],
        log_p_curr[:active_next] + updated_log_p_prev
    )
    return active_next, log_p_next 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def adam(self, param, grad, updates, sample_idx=None, epsilon=1e-6):
        v1 = np.float32(self.decay)
        v2 = np.float32(1.0 - self.decay)
        acc = theano.shared(param.get_value(borrow=False) * 0., borrow=True)
        meang = theano.shared(param.get_value(borrow=False) * 0., borrow=True)
        countt = theano.shared(param.get_value(borrow=False) * 0., borrow=True)
        if sample_idx is None:
            acc_new = v1 * acc + v2 * grad ** 2
            meang_new = v1 * meang + v2 * grad
            countt_new = countt + 1
            updates[acc] = acc_new
            updates[meang] = meang_new
            updates[countt] = countt_new
        else:
            acc_s = acc[sample_idx]
            meang_s = meang[sample_idx]
            countt_s = countt[sample_idx]
            acc_new = v1 * acc_s + v2 * grad ** 2
            meang_new = v1 * meang_s + v2 * grad
            countt_new = countt_s + 1.0
            updates[acc] = T.set_subtensor(acc_s, acc_new)
            updates[meang] = T.set_subtensor(meang_s, meang_new)
            updates[countt] = T.set_subtensor(countt_s, countt_new)
        return (meang_new / (1 - v1 ** countt_new)) / (T.sqrt(acc_new / (1 - v1 ** countt_new)) + epsilon) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def adadelta(self, param, grad, updates, sample_idx=None, epsilon=1e-6):
        v1 = np.float32(self.decay)
        v2 = np.float32(1.0 - self.decay)
        acc = theano.shared(param.get_value(borrow=False) * 0., borrow=True)
        upd = theano.shared(param.get_value(borrow=False) * 0., borrow=True)
        if sample_idx is None:
            acc_new = acc + grad ** 2
            updates[acc] = acc_new
            grad = T.sqrt(upd + epsilon) * grad
            upd_new = v1 * upd + v2 * grad ** 2
            updates[upd] = upd_new
        else:
            acc_s = acc[sample_idx]
            acc_new = acc_s + grad ** 2
            updates[acc] = T.set_subtensor(acc_s, acc_new)
            upd_s = upd[sample_idx]
            upd_new = v1 * upd_s + v2 * grad ** 2
            updates[upd] = T.set_subtensor(upd_s, upd_new)
            grad = T.sqrt(upd_s + epsilon) * grad
        gradient_scaling = T.cast(T.sqrt(acc_new + epsilon), theano.config.floatX)
        return grad / gradient_scaling 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def set_output(self):
        output_shape = self._output_shape
        padding = self._padding
        unpool_size = self._unpool_size
        unpooled_output = tensor.alloc(0.0,  # Value to fill the tensor
                                       output_shape[0],
                                       output_shape[1] + 2 * padding[0],
                                       output_shape[2],
                                       output_shape[3] + 2 * padding[1],
                                       output_shape[4] + 2 * padding[2])

        unpooled_output = tensor.set_subtensor(unpooled_output[:, padding[0]:output_shape[
            1] + padding[0]:unpool_size[0], :, padding[1]:output_shape[3] + padding[1]:unpool_size[
                1], padding[2]:output_shape[4] + padding[2]:unpool_size[2]],
                                               self._prev_layer.output)
        self._output = unpooled_output 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def inpainting_sample_and_noise(self, X, default_input_include_prob=1., default_input_scale=1.):
        # Very hacky! Specifically for inpainting right half of CIFAR-10 given left half
        # assumes X is b01c
        assert X.ndim == 4
        input_space = self.mlp.get_input_space()
        n = input_space.get_total_dimension()
        image_size = input_space.shape[0]
        half_image = int(image_size / 2)
        data_shape = (X.shape[0], image_size, half_image, input_space.num_channels)

        noise = self.theano_rng.normal(size=data_shape, dtype='float32')
        Xg = T.set_subtensor(X[:,:,half_image:,:], noise)
        sampled_part, noise =  self.mlp.dropout_fprop(Xg, default_input_include_prob=default_input_include_prob, default_input_scale=default_input_scale), noise
        sampled_part = sampled_part.reshape(data_shape)
        rval = T.set_subtensor(X[:, :, half_image:, :], sampled_part)
        return rval, noise 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def test_wrong_broadcast(self):
        a = tt.col()
        increment = tt.vector()

        # These symbolic graphs legitimate, as long as increment has exactly
        # one element. So it should fail at runtime, not at compile time.
        rng = numpy.random.RandomState(utt.fetch_seed())

        def rng_randX(*shape):
            return rng.rand(*shape).astype(theano.config.floatX)

        for op in (tt.set_subtensor, tt.inc_subtensor):
            for base in (a[:], a[0]):
                out = op(base, increment)
                f = theano.function([a, increment], out)
                # This one should work
                f(rng_randX(3, 1), rng_randX(1))
                # These ones should not
                self.assertRaises(ValueError,
                                  f, rng_randX(3, 1), rng_randX(2))
                self.assertRaises(ValueError,
                                  f, rng_randX(3, 1), rng_randX(3))
                self.assertRaises(ValueError,
                                  f, rng_randX(3, 1), rng_randX(0)) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def expand_empty(tensor_var, size):
    """
    Transforms the shape of a tensor from (d1, d2 ... ) to ( d1+size, d2, ..)
    by adding uninitialized memory at the end of the tensor.

    """

    if size == 0:
        return tensor_var
    shapes = [tensor_var.shape[x] for x in xrange(tensor_var.ndim)]
    new_shape = [size + shapes[0]] + shapes[1:]
    empty = tensor.AllocEmpty(tensor_var.dtype)(*new_shape)

    ret = tensor.set_subtensor(empty[:shapes[0]], tensor_var)
    ret.tag.nan_guard_mode_check = False
    return ret 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def test_advset_subtensor1():
    """ Test GPU version of set_subtensor on vectors (uses GpuAdvancedIncSubtensor1) """
    shp = (10,)
    shared = cuda.shared_constructor
    xval = numpy.arange(shp[0], dtype='float32').reshape(shp) + 1
    idxs = numpy.array([0,2,5,7,3], dtype='int32')
    yval = numpy.ones(len(idxs), dtype='float32')*10
    x = shared(xval, name='x')
    y = T.tensor(dtype='float32', broadcastable=(False,) * len(shp), name='y')
    expr = T.advanced_set_subtensor1(x, y, idxs)
    f = theano.function([y], expr, mode=mode_with_gpu)
    assert sum([isinstance(node.op, cuda.GpuAdvancedIncSubtensor1)
                for node in f.maker.fgraph.toposort()]) == 1
    rval = f(yval)
    rep = xval.copy()
    rep[idxs] = yval
    utt.assert_allclose(rval, rep) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def test_advset_subtensor1_2d():
    """ Test GPU version of set_subtensor on matrices (uses GpuAdvancedIncSubtensor1_dev20 if compute capability >= 2.0) """
    shp = (10,5)
    shared = cuda.shared_constructor
    xval = numpy.arange(numpy.prod(shp), dtype='float32').reshape(shp) + 1
    idxs = numpy.array([0,2,5,7,3], dtype='int32')
    yval = numpy.ones((len(idxs), shp[1]), dtype='float32')*10
    x = shared(xval, name='x')
    y = T.tensor(dtype='float32', broadcastable=(False,) * len(shp), name='y')
    expr = T.advanced_set_subtensor1(x, y, idxs)
    f = theano.function([y], expr, mode=mode_with_gpu)
    assert sum([isinstance(node.op, cuda.GpuAdvancedIncSubtensor1)
                for node in f.maker.fgraph.toposort()]) == 1
    rval = f(yval)
    rep = xval.copy()
    rep[idxs] = yval
    utt.assert_allclose(rval, rep) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def adagrad_update(self, cost, learning_rate, eps=1e-8):
        params = [ p if p != self.slices else self.EMB for p in self.params ]
        accumulators = [ theano.shared(numpy.zeros(p.get_value(borrow=True).shape,
                                                dtype=theano.config.floatX))
                         for p in params ]
        gparams = [ T.grad(cost, param) for param in self.params ]
        self.gparams = gparams
        updates = [ ]
        for param, gparam, acc in zip(self.params, gparams, accumulators):
            if param == self.slices:
                acc_slices = acc[self.x.flatten()]
                new_acc_slices = acc_slices + gparam**2
                updates.append( (acc, T.set_subtensor(acc_slices, new_acc_slices)) )
                updates.append( (self.EMB, T.inc_subtensor(param,
                                 - learning_rate * gparam / T.sqrt(new_acc_slices+eps))) )
            else:
                new_acc = acc + gparam**2
                updates.append( (acc, new_acc) )
                updates.append( (param, param - learning_rate * gparam /
                                    T.sqrt(new_acc + eps)) )
        return updates 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def ConvByPattern(x, patterns, mask=None):
    W = np.transpose(patterns, (3, 0, 1, 2))
    out2 = T.nnet.conv2d(x.dimshuffle(0, 3, 1, 2), W, filter_shape=W.shape, border_mode='half')
    if mask is not None:
        ## mask has shape (batchSize, #rows_to_be_masked, nCols)

        ## a subtensor of out2 along the horiz direction
        out2_sub_horiz = out2[:, :, :mask.shape[1], :]
        mask_horiz = mask.dimshuffle(0, 'x', 1, 2)
        out3 = T.set_subtensor(out2_sub_horiz, T.mul(out2_sub_horiz, mask_horiz) )

        ## a subtensor of out3 along the vertical direction
        out3_sub_vertical = out3[:, :, :, :mask.shape[1] ]
        mask_vertical = mask.dimshuffle(0, 'x', 2, 1)
        y = T.set_subtensor(out3_sub_vertical, T.mul(out3_sub_vertical, mask_vertical) )
    else:
	y = out2

    y = y.dimshuffle(0, 2, 3, 1)

    return y/np.prod(patterns.shape[1:3]) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def combine_fragments_to_dense_bxcyz(self, tensor, sh):
      """ expected shape: (batch, x, channels, y, z)"""
      ttensor = tensor # be same shape as result, no significant time cost

      output_stride = self.output_stride
      if isinstance(output_stride, list) or isinstance(output_stride, tuple):
          example_stride = np.prod(output_stride)#**3
      else:
          example_stride = output_stride**3
          output_stride  = np.asarray((output_stride,)*3)
      zero = np.array((0), dtype=theano.config.floatX)
      embedding = T.alloc( zero, 1, sh[1]*output_stride[0], sh[2], sh[3]*output_stride[1], sh[4]*output_stride[2]) # first arg. is fill-value (0 in this case) and not an element of the shape
      ix = offset_map(output_stride)
      print "      output_stride",output_stride
      print "      example_stride",example_stride
      for i,(n,m,k) in enumerate(ix):
          embedding = T.set_subtensor(embedding[:,n::output_stride[0],:,m::output_stride[1],k::output_stride[2]], ttensor[i::example_stride])
      return embedding 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def depool(X, factor=2):
    """
    Luke perforated upsample: http://www.brml.org/uploads/tx_sibibtex/281.pdf
    """
    output_shape = [
        X.shape[1],
        X.shape[2]*factor,
        X.shape[3]*factor
    ]
    stride = X.shape[2]
    offset = X.shape[3]
    in_dim = stride * offset
    out_dim = in_dim * factor * factor

    upsamp_matrix = T.zeros((in_dim, out_dim))
    rows = T.arange(in_dim)
    cols = rows*factor + (rows/stride * factor * offset)
    upsamp_matrix = T.set_subtensor(upsamp_matrix[rows, cols], 1.)

    flat = T.reshape(X, (X.shape[0], output_shape[0], X.shape[2] * X.shape[3]))

    up_flat = T.dot(flat, upsamp_matrix)
    upsamp = T.reshape(up_flat, (X.shape[0], output_shape[0], output_shape[1], output_shape[2]))

    return upsamp 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def temporal_padding(x, padding=(1, 1)):
    """Pad the middle dimension of a 3D tensor
    with "padding" zeros left and right.

    Apologies for the inane API, but Theano makes this
    really hard.
    """
    assert len(padding) == 2
    input_shape = x.shape
    output_shape = (input_shape[0],
                    input_shape[1] + padding[0] + padding[1],
                    input_shape[2])
    output = T.zeros(output_shape)
    result = T.set_subtensor(output[:, padding[0]:x.shape[1] + padding[0], :], x)
    if hasattr(x, '_keras_shape'):
        result._keras_shape = (x._keras_shape[0],
                               x._keras_shape[1] + py_sum(padding),
                               x._keras_shape[2])
    return result 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import set_subtensor [as 别名]
def rmsprop(self, param, grad, updates, sample_idx=None, epsilon=1e-6):
        v1 = np.float32(self.adapt_params[0])
        v2 = np.float32(1.0 - self.adapt_params[0])
        acc = theano.shared(param.get_value(borrow=False) * 0., borrow=True)
        if sample_idx is None:
            acc_new = v1 * acc + v2 * grad ** 2
            updates[acc] = acc_new
        else:
            acc_s = acc[sample_idx]
            #            acc_new = v1 * acc_s + v2 * grad ** 2 #Faster, but inaccurate when an index occurs multiple times
            #            updates[acc] = T.set_subtensor(acc_s, acc_new) #Faster, but inaccurate when an index occurs multiple times
            updates[acc] = T.inc_subtensor(T.set_subtensor(acc_s, acc_s * v1)[sample_idx],
                                           v2 * grad ** 2)  # Slower, but accurate when an index occurs multiple times
            acc_new = updates[acc][sample_idx]  # Slower, but accurate when an index occurs multiple times
        gradient_scaling = T.cast(T.sqrt(acc_new + epsilon), theano.config.floatX)
        return grad / gradient_scaling