def call(self, inputs):
def apply_separate_filter_for_each_batch(inputs):
kernel = inputs[1]
x = K.expand_dims(inputs[0], axis=0)
outputs = K.conv2d(
x,
kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.bias is not None:
bias = inputs[2]
outputs = K.bias_add(outputs, bias, data_format=self.data_format)
return K.squeeze(outputs, axis=0)
x = inputs[0]
classes = K.squeeze(inputs[1], axis=1)
if self.bias is not None:
outputs = K.map_fn(apply_separate_filter_for_each_batch,
[x, K.gather(self.kernel, classes), K.gather(self.bias, classes)], dtype='float32')
else:
outputs = K.map_fn(apply_separate_filter_for_each_batch,
[x, K.gather(self.kernel, classes)], dtype='float32')
if self.activation is not None:
return self.activation(outputs)
return outputs
def slide_sum(paras):
_embedded_results = paras[0]
_categories_size_input = paras[1]
def fn(elements):
_embedded_results_ = elements[0]
_categories_size_input_ = K.cast(K.squeeze(elements[1], axis=0), tf.int32)
# 具体原因未知：bug: From merging shape 0 with other shapes. for 'model_2_2/map/while/strided_slice/stack_1' (op: 'Pack') with input shapes: [1], [].
if len(_categories_size_input_.shape) == 1:
_categories_size_input_ = K.cast(K.squeeze(_categories_size_input_, axis=0), tf.int32)
# print('_embedded_results_1',_embedded_results_)
# print('_categories_size_input_', _categories_size_input_)
# def slice2D(x, index):
# return x[150-index:, :]
# embedded_results_ = Lambda(slice2D, arguments={'index':_categories_size_input_})(_embedded_results_) # 切片 2D
_embedded_results_ = _embedded_results_[MAX_SEQUENCE_LENGTH - _categories_size_input_:, :] # 切片 2D
# print('_embedded_results_2',_embedded_results_)
_embedded_results_ = Lambda(lambda x: K.sum(x, axis=0))(_embedded_results_)
# print('_embedded_results_3', _embedded_results_)
return _embedded_results_
return K.map_fn(fn, (_embedded_results, _categories_size_input), dtype=(tf.float32))
def get_cluster_centroids(self):
weight_min = tf.reduce_min(self.weights)
weight_max = tf.reduce_max(self.weights)
# Calculating interpolation nodes, +/- 0.01 is introduced to guarantee that
# CDF will have 0 and 1 and the first and last value respectively.
# The value 30 is a guess. We just need a sufficiently large number here
# since we are going to interpolate values linearly anyway and the initial
# guess will drift away. For these reasons we do not really
# care about the granularity of the lookup.
cdf_x_grid = tf.linspace(weight_min - 0.01, weight_max + 0.01, 30)
f = TFCumulativeDistributionFunction(weights=self.weights)
cdf_values = k.map_fn(f.get_cdf_value, cdf_x_grid)
probability_space = tf.linspace(0 + 0.01, 1, self.number_of_clusters)
# Use upper-bound algorithm to find the appropriate bounds
matching_indices = tf.searchsorted(sorted_sequence=cdf_values,
values=probability_space,
side='right')
# Interpolate linearly between every found indices I at position using I at
# pos n-1 as a second point. The value of x is a new cluster centroid
def get_single_centroid(i):
i_clipped = tf.minimum(i, tf.size(cdf_values) - 1)
i_previous = tf.maximum(0, i_clipped - 1)
s = TFLinearEquationSolver(x1=cdf_x_grid[i_clipped],
y1=cdf_values[i_clipped],
x2=cdf_x_grid[i_previous],
y2=cdf_values[i_previous])
y = cdf_values[i_clipped]
single_centroid = s.solve_for_x(y)
return single_centroid
centroids = k.map_fn(get_single_centroid,
matching_indices,
dtype=tf.float32)
cluster_centroids = tf.reshape(centroids, (self.number_of_clusters, ))
return cluster_centroids
def __call__(self, w):
w_shape = w.shape
if w_shape.rank is None or w_shape.rank != 4:
raise ValueError(
'The weight tensor must be of rank 4, but is of shape: %s' %
w_shape)
height, width, channels, kernels = w_shape
w = K.reshape(w, (height, width, channels * kernels))
# TODO(cpeter): Switch map_fn for a faster tf.vectorized_map once K.switch
# is supported.
w = K.map_fn(self._kernel_constraint,
K.stack(array_ops.unstack(w, axis=-1), axis=0))
return K.reshape(K.stack(array_ops.unstack(w, axis=0), axis=-1),
(height, width, channels, kernels))