Python tensorflow.pack函数代码示例

def FixedUnPooling(x, shape, unpool_mat=None):
    """
    Unpool the input with a fixed mat to perform kronecker product with.

    :param input: NHWC tensor
    :param shape: int or [h, w]
    :param unpool_mat: a tf/np matrix with size=shape. If None, will use a mat
        with 1 at top-left corner.
    :returns: NHWC tensor
    """
    shape = shape2d(shape)
    input_shape = tf.shape(x)
    if unpool_mat is None:
        mat = np.zeros(shape, dtype='float32')
        mat[0][0] = 1
        unpool_mat = tf.Variable(mat, trainable=False, name='unpool_mat')
    elif isinstance(unpool_mat, np.ndarray):
        unpool_mat = tf.Variable(unpool_mat, trainable=False, name='unpool_mat')
    assert unpool_mat.get_shape().as_list() == list(shape)

    # perform a tensor-matrix kronecker product
    fx = flatten(tf.transpose(x, [0, 3, 1, 2]))
    fx = tf.expand_dims(fx, -1)       # (bchw)x1
    mat = tf.expand_dims(flatten(unpool_mat), 0)    #1x(shxsw)
    prod = tf.matmul(fx, mat)    #(bchw) x(shxsw)
    prod = tf.reshape(prod, tf.pack(
        [-1, input_shape[3], input_shape[1], input_shape[2], shape[0], shape[1]]))
    prod = tf.transpose(prod, [0, 2, 4, 3, 5, 1])
    prod = tf.reshape(prod, tf.pack(
        [-1, input_shape[1] * shape[0], input_shape[2] * shape[1], input_shape[3]]))
    return prod

  def iou(self, boxes1, boxes2):
    """calculate ious
    Args:
      boxes1: 4-D tensor [CELL_SIZE, CELL_SIZE, BOXES_PER_CELL, 4]  ====> (x_center, y_center, w, h)
      boxes2: 1-D tensor [4] ===> (x_center, y_center, w, h)
    Return:
      iou: 3-D tensor [CELL_SIZE, CELL_SIZE, BOXES_PER_CELL]
    """
    boxes1 = tf.pack([boxes1[:, :, :, 0] - boxes1[:, :, :, 2] / 2, boxes1[:, :, :, 1] - boxes1[:, :, :, 3] / 2,
                      boxes1[:, :, :, 0] + boxes1[:, :, :, 2] / 2, boxes1[:, :, :, 1] + boxes1[:, :, :, 3] / 2])
    boxes1 = tf.transpose(boxes1, [1, 2, 3, 0])
    boxes2 =  tf.pack([boxes2[0] - boxes2[2] / 2, boxes2[1] - boxes2[3] / 2,
                      boxes2[0] + boxes2[2] / 2, boxes2[1] + boxes2[3] / 2])

    #calculate the left up point
    lu = tf.maximum(boxes1[:, :, :, 0:2], boxes2[0:2])
    rd = tf.minimum(boxes1[:, :, :, 2:], boxes2[2:])

    #intersection
    intersection = rd - lu 

    inter_square = intersection[:, :, :, 0] * intersection[:, :, :, 1]

    mask = tf.cast(intersection[:, :, :, 0] > 0, tf.float32) * tf.cast(intersection[:, :, :, 1] > 0, tf.float32)
    
    inter_square = mask * inter_square
    
    #calculate the boxs1 square and boxs2 square
    square1 = (boxes1[:, :, :, 2] - boxes1[:, :, :, 0]) * (boxes1[:, :, :, 3] - boxes1[:, :, :, 1])
    square2 = (boxes2[2] - boxes2[0]) * (boxes2[3] - boxes2[1])
    
    return inter_square/(square1 + square2 - inter_square + 1e-6)

  def _define_distance_to_clusters(self, data):
    """Defines the Mahalanobis distance to the assigned Gaussian."""
    # TODO(xavigonzalvo): reuse (input - mean) * cov^-1 * (input -
    # mean) from log probability function.
    self._all_scores = []
    for shard in data:
      all_scores = []
      shard = tf.expand_dims(shard, 0)
      for c in xrange(self._num_classes):
        if self._covariance_type == FULL_COVARIANCE:
          cov = self._covs[c, :, :]
        elif self._covariance_type == DIAG_COVARIANCE:
          cov = tf.diag(self._covs[c, :])
        inverse = tf.matrix_inverse(cov + self._min_var)
        inv_cov = tf.tile(
            tf.expand_dims(inverse, 0),
            tf.pack([self._num_examples, 1, 1]))
        diff = tf.transpose(shard - self._means[c, :, :], perm=[1, 0, 2])
        m_left = tf.batch_matmul(diff, inv_cov)
        all_scores.append(tf.sqrt(tf.batch_matmul(
            m_left, tf.transpose(diff, perm=[0, 2, 1])
        )))
      self._all_scores.append(tf.reshape(
          tf.concat(1, all_scores),
          tf.pack([self._num_examples, self._num_classes])))

    # Distance to the associated class.
    self._all_scores = tf.concat(0, self._all_scores)
    assignments = tf.concat(0, self.assignments())
    rows = tf.to_int64(tf.range(0, self._num_examples))
    indices = tf.concat(1, [tf.expand_dims(rows, 1),
                            tf.expand_dims(assignments, 1)])
    self._scores = tf.gather_nd(self._all_scores, indices)

    def build_reparam_loss_kl(self):
        """Build loss function. Its automatic differentiation
        is a stochastic gradient of

        .. math::

            -ELBO =  - ( E_{q(z; \lambda)} [ \log p(x | z) ]
                        + KL(q(z; \lambda) || p(z)) )

        based on the reparameterization trick. (Kingma and Welling, 2014)

        It assumes the KL is analytic.

        It assumes the prior is :math:`p(z) = \mathcal{N}(z; 0, 1)`

        Computed by sampling from :math:`q(z;\lambda)` and evaluating the
        expectation using Monte Carlo sampling.
        """
        x = self.data
        z = self.variational.sample(self.n_samples)

        mu = tf.pack([layer.loc for layer in self.variational.layers])
        sigma = tf.pack([layer.scale for layer in self.variational.layers])
        self.loss = tf.reduce_mean(self.model.log_lik(x, z)) - \
                    kl_multivariate_normal(mu, sigma)
        return -self.loss

def inputs(path):
  whole = read_csv(FLAGS.batch_size, path)
  features = tf.transpose(tf.pack(whole[0:FLAGS.max_sentence_len]))
  label = tf.one_hot(
      tf.transpose(tf.pack(whole[FLAGS.max_sentence_len])),
      depth=2)
  return features, label

    def build_model(self):
        video = tf.placeholder(tf.float32, [self.batch_size, self.n_lstm_steps, self.dim_image])
        video_mask = tf.placeholder(tf.float32, [self.batch_size, self.n_lstm_steps])

        HLness = tf.placeholder(tf.int32, [self.batch_size, self.n_lstm_steps])
        HLness_mask = tf.placeholder(tf.float32, [self.batch_size, self.n_lstm_steps])

        video_flat = tf.reshape(video, [-1, self.dim_image])
        image_emb = tf.nn.xw_plus_b( video_flat, self.encode_image_W, self.encode_image_b) # (batch_size*n_lstm_steps, dim_hidden)
        image_emb = tf.reshape(image_emb, [self.batch_size, self.n_lstm_steps, self.dim_hidden])
        image_emb = tf.transpose(image_emb, [1,0,2]) # n x b x h

        state2 = tf.zeros([self.batch_size, self.lstm2.state_size])

	loss_HL = 0.0
	_X = tf.reshape(image_emb, [-1, self.dim_hidden]) # (n x b) x h
	_X = tf.split(0, self.n_lstm_steps, _X) # n x (b x h)
	[output2, state2] = rnn.rnn(self.lstm_HL_net,_X,dtype=tf.float32) # n x (b x h)
	output2 = tf.transpose(tf.pack(output2), [1,0,2]) # b x n x h
	onehot_labels = []
	logit_words = []
	indices = tf.expand_dims(tf.range(0, self.n_lstm_steps, 1), 1) # n x 1
	for ii in xrange(10):
		labels = tf.expand_dims(HLness[ii,:], 1) # n x 1
		concated = tf.concat(1, [indices, labels]) # n x 2
		onehot_labels = tf.sparse_to_dense(concated, tf.pack([self.n_lstm_steps, 2]), 1.0, 0.0) # n x 2
		logit_words = tf.nn.xw_plus_b(output2[ii,:,:], self.embed_HL_W, self.embed_HL_b) # n x 2
		cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logit_words, onehot_labels) # n x 1
		cross_entropy = tf.mul(cross_entropy, HLness_mask[ii,:]) # n x 1
		loss_HL += tf.reduce_sum(cross_entropy) # 1

	loss_HL = loss_HL / tf.reduce_sum(HLness_mask)
	loss = loss_HL
        return loss, video, video_mask, HLness, HLness_mask

    def log_prob(self, xs, zs):
        """Return a vector [log p(xs, zs[1,:]), ..., log p(xs, zs[S,:])]."""
        x = xs['x']
        pi, mus, sigmas = zs
        log_prior = dirichlet.logpdf(pi, self.alpha)
        log_prior += tf.reduce_sum(norm.logpdf(mus, 0, np.sqrt(self.c)), 1)
        log_prior += tf.reduce_sum(invgamma.logpdf(sigmas, self.a, self.b), 1)

        # Loop over each sample zs[s, :].
        log_lik = []
        N = get_dims(x)[0]
        n_samples = get_dims(pi)[0]
        for s in range(n_samples):
            # log-likelihood is
            # sum_{n=1}^N log sum_{k=1}^K exp( log pi_k + log N(x_n; mu_k, sigma_k) )
            # Create a K x N matrix, whose entry (k, n) is
            # log pi_k + log N(x_n; mu_k, sigma_k).
            matrix = []
            for k in range(self.K):
                matrix += [tf.ones(N)*tf.log(pi[s, k]) +
                           multivariate_normal.logpdf(x,
                               mus[s, (k*self.D):((k+1)*self.D)],
                               sigmas[s, (k*self.D):((k+1)*self.D)])]

            matrix = tf.pack(matrix)
            # log_sum_exp() along the rows is a vector, whose nth
            # element is the log-likelihood of data point x_n.
            vector = log_sum_exp(matrix, 0)
            # Sum over data points to get the full log-likelihood.
            log_lik_z = tf.reduce_sum(vector)
            log_lik += [log_lik_z]

        return log_prior + tf.pack(log_lik)

 def _composition_function(self, inputs, length, init_state=None):
     if self._composition == "GRU":
         cell = GRUCell(self._size)
         return dynamic_rnn(cell, inputs, sequence_length=length, time_major=True,
                            initial_state=init_state, dtype=tf.float32)[0]
     elif self._composition == "LSTM":
         cell = BasicLSTMCell(self._size)
         init_state = tf.concat(1, [tf.zeros_like(init_state, tf.float32), init_state]) if init_state else None
         outs = dynamic_rnn(cell, inputs, sequence_length=length, time_major=True,
                            initial_state=init_state, dtype=tf.float32)[0]
         return outs
     elif self._composition == "BiGRU":
         cell = GRUCell(self._size // 2, self._size)
         init_state_fw, init_state_bw = tf.split(1, 2, init_state) if init_state else (None, None)
         with tf.variable_scope("forward"):
             fw_outs = dynamic_rnn(cell, inputs, sequence_length=length, time_major=True,
                                   initial_state=init_state_fw, dtype=tf.float32)[0]
         with tf.variable_scope("backward"):
             rev_inputs = tf.reverse_sequence(tf.pack(inputs), length, 0, 1)
             rev_inputs = [tf.reshape(x, [-1, self._size]) for x in tf.split(0, len(inputs), rev_inputs)]
             bw_outs = dynamic_rnn(cell, rev_inputs, sequence_length=length, time_major=True,
                                   initial_state=init_state_bw, dtype=tf.float32)[0]
             bw_outs = tf.reverse_sequence(tf.pack(bw_outs), length, 0, 1)
             bw_outs = [tf.reshape(x, [-1, self._size]) for x in tf.split(0, len(inputs), bw_outs)]
         return [tf.concat(1, [fw_out, bw_out]) for fw_out, bw_out in zip(fw_outs, bw_outs)]
     else:
         raise NotImplementedError("Other compositions not implemented yet.")

    def compute_loss(self,emb_batch,curr_batch_size=None):
        outloss=[]
        prediction=[]
        for idx_batch in range(self.config.batch_size):

            tree_states=self.compute_states(emb_batch,idx_batch)
            logits = self.create_output(tree_states)

            labels1=tf.gather(self.labels,idx_batch)
            labels2=tf.reduce_sum(tf.to_int32(tf.not_equal(labels1,-1)))
            labels=tf.gather(labels1,tf.range(labels2))
            loss = self.calc_loss(logits,labels)


            pred = tf.nn.softmax(logits)

            pred_root=tf.gather(pred,labels2-1)


            prediction.append(pred_root)
            outloss.append(loss)

        batch_loss=tf.pack(outloss)
        self.pred = tf.pack(prediction)

        return batch_loss

def inference1(data):
    data_shape_l = data.get_shape().as_list()
    with tf.variable_scope('conv1') as scope:
        weights = _variable_with_weight_decay('weights', shape=[3, 3, 3, 32],wd=0.0)
        biases = _variable_on_cpu('biases', [32], tf.constant_initializer(0.0))
        h_conv1 = _conv2d(data, weights, biases, [1,2,2,1])
      
    with tf.variable_scope('conv2') as scope:
        weights = _variable_with_weight_decay('weights', shape=[3, 3, 32, 32],wd=0.0)
        biases = _variable_on_cpu('biases', [32], tf.constant_initializer(0.0))
        h_conv2 = _conv2d(h_conv1, weights, biases, [1,1,1,1])

    with tf.variable_scope('deconv1') as scope:
        weights = _variable_with_weight_decay('weights', shape=[3, 3, 32, 32],wd=0.0)
        biases = _variable_on_cpu('biases', [32], tf.constant_initializer(0.0))
        output_shape = tf.pack(h_conv1.get_shape().as_list())
        h_dconv1 = _dconv2d(h_conv2, weights, biases, output_shape, [1,1,1,1])

    with tf.variable_scope('deconv2') as scope:
        weights = _variable_with_weight_decay('weights', shape=[3, 3, 3, 32],wd=0.0)
        biases = _variable_on_cpu('biases', [3], tf.constant_initializer(0.0))
        output_shape = tf.pack(data_shape_l)
        h_dconv2 = _dconv2d(h_dconv1, weights, biases, output_shape, [1,2,2,1])

    # with tf.variable_scope('deconv1') as scope:
    #     weights = _variable_with_weight_decay('weights', shape=[3, 3, 3, 32],
    #                                        stddev=1e-4, wd=0.0)
    #     biases = _variable_on_cpu('biases', [3], tf.constant_initializer(0.0))
    #     output_shape = tf.pack(data_shape_l)
    #     h_dconv1 = _dconv2d(h_conv1, weights, biases, output_shape, [1,2,2,1])
    return h_dconv2

 def lstm_cell(i, o, state):
   """
   Create a LSTM cell. See e.g.: http://arxiv.org/pdf/1402.1128v1.pdf
   Note that in this formulation, we omit the various connections between the
   previous state and the gates.
   """                   
   i_list = tf.pack([i, i, i, i])
   #print i_list.get_shape().as_list()
   o_list = tf.pack([o, o, o, o])
                         
   ins = tf.batch_matmul(i_list, fico_x)
   outs = tf.batch_matmul(o_list, fico_m)
   
   h_x = ins + outs + fico_b
   #print h_x.get_shape().as_list()
   
   #forget_gate = tf.sigmoid(tf.matmul(i, fx) + tf.matmul(o, fm) + fb)
   forget_gate = tf.sigmoid(h_x[0,:,:])
   
   #input_gate = tf.sigmoid(tf.matmul(i, ix) + tf.matmul(o, im) + ib)
   input_gate = tf.sigmoid(h_x[1,:,:])
   
   #update = tf.tanh(tf.matmul(i, cx) + tf.matmul(o, cm) + cb)
   update = tf.tanh(h_x[2,:,:])
   
   state = forget_gate*state + input_gate*update
   
   #output_gate = tf.sigmoid(tf.matmul(i, ox) + tf.matmul(o, om) + ob)
   output_gate = tf.sigmoid(h_x[3,:,:])
   
   h = output_gate * tf.tanh(state)
   #print 'h', h.get_shape().as_list()
   return h, state

 def _build_annealed_losses(self, outputs, labels, anneal_factors):
     sequence_length = len(outputs)
     packed_outputs = tf.pack(outputs)
     tiled_labels = tf.pack([labels for i in range(sequence_length)])
     accumulated_losses = -tf.reduce_sum(tiled_labels * tf.log(packed_outputs), [1, 2])
     annealed_losses = tf.mul(anneal_factors, tf.concat(0, accumulated_losses))
     return annealed_losses

    def build_predict(self, Xnew, full_cov=False):
        """
        Compute the mean and variance of the latent function at some new points
        Xnew. Note that this is very similar to the SGPR prediction, for whcih
        there are notes in the SGPR notebook.
        """
        num_inducing = tf.shape(self.Z)[0]
        psi0, psi1, psi2 = ke.build_psi_stats(self.Z, self.kern, self.X_mean, self.X_var)
        Kuu = self.kern.K(self.Z) + eye(num_inducing) * 1e-6
        Kus = self.kern.K(self.Z, Xnew)
        sigma2 = self.likelihood.variance
        sigma = tf.sqrt(sigma2)
        L = tf.cholesky(Kuu)

        A = tf.matrix_triangular_solve(L, tf.transpose(psi1), lower=True) / sigma
        tmp = tf.matrix_triangular_solve(L, psi2, lower=True)
        AAT = tf.matrix_triangular_solve(L, tf.transpose(tmp), lower=True) / sigma2
        B = AAT + eye(num_inducing)
        LB = tf.cholesky(B)
        c = tf.matrix_triangular_solve(LB, tf.matmul(A, self.Y), lower=True) / sigma
        tmp1 = tf.matrix_triangular_solve(L, Kus, lower=True)
        tmp2 = tf.matrix_triangular_solve(LB, tmp1, lower=True)
        mean = tf.matmul(tf.transpose(tmp2), c)
        if full_cov:
            var = self.kern.K(Xnew) + tf.matmul(tf.transpose(tmp2), tmp2)\
                - tf.matmul(tf.transpose(tmp1), tmp1)
            shape = tf.pack([1, 1, tf.shape(self.Y)[1]])
            var = tf.tile(tf.expand_dims(var, 2), shape)
        else:
            var = self.kern.Kdiag(Xnew) + tf.reduce_sum(tf.square(tmp2), 0)\
                - tf.reduce_sum(tf.square(tmp1), 0)
            shape = tf.pack([1, tf.shape(self.Y)[1]])
            var = tf.tile(tf.expand_dims(var, 1), shape)
        return mean + self.mean_function(Xnew), var

def _rnn_template(incoming, cell, dropout=None, return_seq=False,
                  return_state=False, initial_state=None, dynamic=False,
                  scope=None, name="LSTM"):
    """ RNN Layer Template. """
    sequence_length = None
    if dynamic:
        sequence_length = retrieve_seq_length_op(
            incoming if isinstance(incoming, tf.Tensor) else tf.pack(incoming))

    input_shape = utils.get_incoming_shape(incoming)

    with tf.variable_op_scope([incoming], scope, name) as scope:
        name = scope.name

        _cell = cell
        # Apply dropout
        if dropout:
            if type(dropout) in [tuple, list]:
                in_keep_prob = dropout[0]
                out_keep_prob = dropout[1]
            elif isinstance(dropout, float):
                in_keep_prob, out_keep_prob = dropout, dropout
            else:
                raise Exception("Invalid dropout type (must be a 2-D tuple of "
                                "float)")
            cell = DropoutWrapper(cell, in_keep_prob, out_keep_prob)

        inference = incoming
        # If a tensor given, convert it to a per timestep list
        if type(inference) not in [list, np.array]:
            ndim = len(input_shape)
            assert ndim >= 3, "Input dim should be at least 3."
            axes = [1, 0] + list(range(2, ndim))
            inference = tf.transpose(inference, (axes))
            inference = tf.unpack(inference)

        outputs, state = _rnn(cell, inference, dtype=tf.float32,
                              initial_state=initial_state, scope=name,
                              sequence_length=sequence_length)

        # Retrieve RNN Variables
        c = tf.GraphKeys.LAYER_VARIABLES + '/' + scope.name
        for v in [_cell.W, _cell.b]:
            if hasattr(v, "__len__"):
                for var in v: tf.add_to_collection(c, var)
            else:
                tf.add_to_collection(c, v)
        # Track activations.
        tf.add_to_collection(tf.GraphKeys.ACTIVATIONS, outputs[-1])

    if dynamic:
        outputs = tf.transpose(tf.pack(outputs), [1, 0, 2])
        o = advanced_indexing_op(outputs, sequence_length)
    else:
        o = outputs if return_seq else outputs[-1]

    # Track output tensor.
    tf.add_to_collection(tf.GraphKeys.LAYER_TENSOR + '/' + name, o)

    return (o, state) if return_state else o

def total_variation_loss(layer):
    shape = tf.shape(layer)
    height = shape[1]
    width = shape[2]
    y = tf.slice(layer, [0,0,0,0], tf.pack([-1,height-1,-1,-1])) - tf.slice(layer, [0,1,0,0], [-1,-1,-1,-1])
    x = tf.slice(layer, [0,0,0,0], tf.pack([-1,-1,width-1,-1])) - tf.slice(layer, [0,0,1,0], [-1,-1,-1,-1])
    return tf.nn.l2_loss(x) / tf.to_float(tf.size(x)) + tf.nn.l2_loss(y) / tf.to_float(tf.size(y))