Python theano.tensor方法代码示例

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def broadcast_concat(tensors, axis):
    """
    Broadcast tensors together, then concatenate along axis
    """
    ndim = tensors[0].ndim
    assert all(t.ndim == ndim for t in tensors), "ndims don't match for broadcast_concat: {}".format(tensors)
    broadcast_shapes = []
    for i in range(ndim):
        if i == axis:
            broadcast_shapes.append(1)
        else:
            dim_size = next((t.shape[i] for t in tensors if not t.broadcastable[i]), 1)
            broadcast_shapes.append(dim_size)
    broadcasted_tensors = []
    for t in tensors:
        tile_reps = [bshape if t.broadcastable[i] else 1 for i,bshape in enumerate(broadcast_shapes)]
        if all(rep is 1 for rep in tile_reps):
            # Don't need to broadcast this tensor
            broadcasted_tensors.append(t)
        else:
            broadcasted_tensors.append(T.tile(t, tile_reps))
    return T.concatenate(broadcasted_tensors, axis) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def reduce_log_sum(tensor, axis=None, guaranteed_finite=False):
    """
    Sum probabilities in the log domain, i.e return
        log(e^vec[0] + e^vec[1] + ...)
        = log(e^x e^(vec[0]-x) + e^x e^(vec[1]-x) + ...)
        = log(e^x [e^(vec[0]-x) + e^(vec[1]-x) + ...])
        = log(e^x) + log(e^(vec[0]-x) + e^(vec[1]-x) + ...)
        = x + log(e^(vec[0]-x) + e^(vec[1]-x) + ...)
    For numerical stability, we choose x = max(vec)
    Note that if x is -inf, that means all values are -inf,
    so the answer should be -inf. In this case, choose x = 0
    """
    maxval = T.max(tensor, axis)
    maxval_full = T.max(tensor, axis, keepdims=True)
    if not guaranteed_finite:
        maxval = T.switch(T.isfinite(maxval), maxval, T.zeros_like(maxval))
        maxval_full = T.switch(T.isfinite(maxval_full), maxval_full, T.zeros_like(maxval_full))
    reduced_sum = T.sum(T.exp(tensor - maxval_full), axis)
    logsum = maxval + T.log(reduced_sum)
    return logsum 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def errors(self, y):
        """Return a float representing the number of errors in the minibatch
        over the total number of examples of the minibatch ; zero one
        loss over the size of the minibatch

        :type y: theano.tensor.TensorType
        :param y: corresponds to a vector that gives for each example the
                  correct label
        """

        # check if y has same dimension of y_pred
        if y.ndim != self.y_pred.ndim:
            raise TypeError(
                'y should have the same shape as self.y_pred',
                ('y', y.type, 'y_pred', self.y_pred.type)
            )
        # check if y is of the correct datatype
        if y.dtype.startswith('int'):
            # the T.neq operator returns a vector of 0s and 1s, where 1
            # represents a mistake in prediction
            return T.mean(T.neq(self.y_pred, y))
        else:
            raise NotImplementedError() 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def __init__(self, random_seed=dt.datetime.now().microsecond, compute_grad=True):
        self.rng = np.random.RandomState(random_seed)

        self.batch_size = cfg.CONST.BATCH_SIZE
        self.img_w = cfg.CONST.IMG_W
        self.img_h = cfg.CONST.IMG_H
        self.n_vox = cfg.CONST.N_VOX
        self.compute_grad = compute_grad

        # (self.batch_size, 3, self.img_h, self.img_w),
        # override x and is_x_tensor4 when using multi-view network
        self.x = tensor.tensor4()
        self.is_x_tensor4 = True

        # (self.batch_size, self.n_vox, 2, self.n_vox, self.n_vox),
        self.y = tensor5()

        self.activations = []  # list of all intermediate activations
        self.loss = []  # final loss
        self.output = []  # final output
        self.error = []  # final output error
        self.params = []  # all learnable params
        self.grads = []  # will be filled out automatically
        self.setup() 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [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: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def shared_dropout_layer(shape, use_noise, trng, value, scaled=True):
    #re-scale dropout at training time, so we don't need to at test time
    if scaled:
        proj = tensor.switch(
            use_noise,
            trng.binomial(shape, p=value, n=1,
                                        dtype='float32')/value,
            theano.shared(numpy.float32(1.)))
    else:
        proj = tensor.switch(
            use_noise,
            trng.binomial(shape, p=value, n=1,
                                        dtype='float32'),
            theano.shared(numpy.float32(value)))
    return proj


# feedforward layer: affine transformation + point-wise nonlinearity 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def build_encoder_bi(tparams, options):
	"""
	build bidirectional encoder, given pre-computed word embeddings
	"""
	# word embedding (source)
	embedding = tensor.tensor3('embedding', dtype='float32')
	embeddingr = embedding[::-1]
	x_mask = tensor.matrix('x_mask', dtype='float32')
	xr_mask = x_mask[::-1]

	# encoder
	proj = get_layer(options['encoder'])[1](tparams, embedding, options,
											prefix='encoder',
											mask=x_mask)
	projr = get_layer(options['encoder'])[1](tparams, embeddingr, options,
											 prefix='encoder_r',
											 mask=xr_mask)

	ctx = tensor.concatenate([proj[0][-1], projr[0][-1]], axis=1)

	return embedding, x_mask, ctx


# some utilities 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def __init__(self, layers, mode='sum'):
        ''' Merge the output of a list of layers or containers into a single tensor.
            mode: {'sum', 'concat'}
        '''
        if len(layers) < 2:
            raise Exception("Please specify two or more input layers (or containers) to merge")
        self.mode = mode
        self.layers = layers
        self.params = []
        self.regularizers = []
        self.constraints = []
        for l in self.layers:
            params, regs, consts = l.get_params()
            self.regularizers += regs
            # params and constraints have the same size
            for p, c in zip(params, consts):
                if p not in self.params:
                    self.params.append(p)
                    self.constraints.append(c) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def adadelta(lr, tparams, grads, inp, cost):
    running_up2 = [theano.shared(p.get_value() * numpy.float32(0.), name='%s_rup2'%k) for k, p in tparams.iteritems()]
    running_grads2 = [theano.shared(p.get_value() * numpy.float32(0.), name='%s_rgrad2'%k) for k, p in tparams.iteritems()]

    rg2_new = [0.95 * rg2 + 0.05 * (g ** 2) for rg2, g in zip(running_grads2, grads)]
    rg2up = [(rg2, r_n) for rg2, r_n in zip(running_grads2, rg2_new)]
    
    
    updir = [-tensor.sqrt(ru2 + 1e-6) / tensor.sqrt(rg2 + 1e-6) * zg for zg, ru2, rg2 in zip(grads, running_up2, rg2_new)]
    ru2up = [(ru2, 0.95 * ru2 + 0.05 * (ud ** 2)) for ru2, ud in zip(running_up2, updir)]
    param_up = [(p, p + ud) for p, ud in zip(itemlist(tparams), updir)]

    inp += [lr]
    f_update = theano.function(inp, cost, updates=rg2up+ru2up+param_up, on_unused_input='ignore', profile=profile)

    return f_update 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def debugging_adadelta(lr, tparams, grads, inp, cost):
    zipped_grads = [theano.shared(p.get_value() * numpy.float32(0.), name='%s_grad'%k) for k, p in tparams.iteritems()]
    running_up2 = [theano.shared(p.get_value() * numpy.float32(0.), name='%s_rup2'%k) for k, p in tparams.iteritems()]
    running_grads2 = [theano.shared(p.get_value() * numpy.float32(0.), name='%s_rgrad2'%k) for k, p in tparams.iteritems()]

    zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
    rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2)) for rg2, g in zip(running_grads2, grads)]

    f_grad_shared = theano.function(inp, cost, updates=zgup+rg2up, profile=profile)
    
    
    updir = [-tensor.sqrt(ru2 + 1e-6) / tensor.sqrt(rg2 + 1e-6) * zg for zg, ru2, rg2 in zip(zipped_grads, running_up2, running_grads2)]
    ru2up = [(ru2, 0.95 * ru2 + 0.05 * (ud ** 2)) for ru2, ud in zip(running_up2, updir)]
    param_up = [(p, p + ud) for p, ud in zip(itemlist(tparams), updir)]

    f_update = theano.function([lr], [], updates=ru2up+param_up, on_unused_input='ignore', profile=profile)

    return f_grad_shared, f_update 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def timeit_2vector_theano(init, nb_element=1e6, nb_repeat=3, nb_call=int(1e2), expr="a**2 + b**2 + 2*a*b"):
    t3 = timeit.Timer("tf(av,bv)",
                      """
import theano
import theano.tensor as T
import numexpr as ne
from theano.tensor import exp
%(init)s
av=a
bv=b
a=T.dvector()
b=T.dvector()
tf= theano.function([a,b],%(expr)s)
"""%locals()
)
    ret=t3.repeat(nb_repeat,nb_call)
    return np.asarray(ret) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def local_csm_properties_csm(node):
    """
    If we find csm_properties(CSM(*args)), then we can replace that with the
    *args directly.

    """
    if node.op == csm_properties:
        csm, = node.inputs
        if csm.owner and (csm.owner.op == CSC or csm.owner.op == CSR):
            # csm.owner.inputs could be broadcastable. In that case, we have
            # to adjust the broadcasting flag here.
            ret_var = [theano.tensor.patternbroadcast(i, o.broadcastable)
                       for i, o in izip(csm.owner.inputs, node.outputs)]
            return ret_var

    return False 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def make_node(self, x, y):
        x, y = sparse.as_sparse_variable(x), tensor.as_tensor_variable(y)
        out_dtype = scalar.upcast(x.type.dtype, y.type.dtype)
        if self.inplace:
            assert out_dtype == y.dtype

        indices, indptr, data = csm_indices(x), csm_indptr(x), csm_data(x)
        # We either use CSC or CSR depending on the format of input
        assert self.format == x.type.format
        # The magic number two here arises because L{scipy.sparse}
        # objects must be matrices (have dimension 2)
        assert y.type.ndim == 2
        out = tensor.TensorType(dtype=out_dtype,
                                broadcastable=y.type.broadcastable)()
        return gof.Apply(self,
                         [data, indices, indptr, y],
                         [out]) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def link(self, input):
        """
        The input has to be a tensor with the right
        most dimension equal to input_dim.
        """
        self.input = input
        self.linear_output = T.dot(self.input, self.weights)
        if self.bias:
            self.linear_output = self.linear_output + self.bias
        if self.activation is None:
            self.output = self.linear_output
        else:
            self.output = self.activation(self.linear_output)
        return self.output
#}}} 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import tensor [as 别名]
def get_initial_states(self, x):
        # build an all-zero tensor of shape (samples, output_dim)
        initial_state = K.zeros_like(x)  # (samples, timesteps, input_dim)
        initial_state = K.sum(initial_state, axis=(1, 2))  # (samples,)
        initial_state = K.expand_dims(initial_state)  # (samples, 1)
        initial_state = K.tile(initial_state, [1, self.output_dim])  # (samples, output_dim)
        initial_states = [initial_state for _ in range(len(self.states))]
        return initial_states