# ______ _ _ _ _ _ _ _
# | ___ \ | | | | (_) (_) | | (_)
# | |_/ / __ ___ | |__ __ _| |__ _| |_ ___| |_ _ ___
# | __/ '__/ _ \| '_ \ / _` | '_ \| | | / __| __| |/ __|
# | | | | | (_) | |_) | (_| | |_) | | | \__ \ |_| | (__
# \_| |_| \___/|_.__/ \__,_|_.__/|_|_|_|___/\__|_|\___|
# ___ ___ _ _
# | \/ | | | (_)
# | . . | ___ ___| |__ __ _ _ __ _ ___ ___
# | |\/| |/ _ \/ __| '_ \ / _` | '_ \| |/ __/ __|
# | | | | __/ (__| | | | (_| | | | | | (__\__ \
# \_| |_/\___|\___|_| |_|\__,_|_| |_|_|\___|___/
# _ _ _
# | | | | | |
# | | __ _| |__ ___ _ __ __ _| |_ ___ _ __ _ _
# | | / _` | '_ \ / _ \| '__/ _` | __/ _ \| '__| | | |
# | |___| (_| | |_) | (_) | | | (_| | || (_) | | | |_| |
# \_____/\__,_|_.__/ \___/|_| \__,_|\__\___/|_| \__, |
# __/ |
# |___/
#
# MIT License
#
# Copyright (c) 2019 Probabilistic Mechanics Laboratory
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# ==============================================================================
""" Core PINN layers
"""
from tensorflow.python.keras.layers import Dense
from tensorflow.python.keras import initializers
from tensorflow.python.keras import regularizers
from tensorflow.python.keras import constraints
from tensorflow.python.keras.engine.base_layer import Layer
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import common_shapes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from pinn.layers import interpolate
from tensorflow import shape, expand_dims, constant, cast
import numpy as np
def getScalingDenseLayer(input_location, input_scale):
recip_input_scale = np.reciprocal(input_scale)
waux = np.diag(recip_input_scale)
baux = -input_location*recip_input_scale
dL = Dense(input_location.shape[0], activation = None, input_shape = input_location.shape)
dL.build(input_shape = input_location.shape)
dL.set_weights([waux, baux])
dL.trainable = False
return dL
def inputsSelection(inputs_shape, ndex):
if not hasattr(ndex,'index'):
ndex = list(ndex)
input_mask = np.zeros([inputs_shape[-1], len(ndex)])
for i in range(inputs_shape[-1]):
for v in ndex:
if i == v:
input_mask[i,ndex.index(v)] = 1
dL = Dense(len(ndex), activation = None, input_shape = inputs_shape,
use_bias = False)
dL.build(input_shape = inputs_shape)
dL.set_weights([input_mask])
dL.trainable = False
return dL
class TableInterpolation(Layer):
""" Table lookup and interpolation implementation.
Interrogates provided query points using provided table and outputs the interpolation result.
Remarks on this class:
- Only supports 2-D tables (f(x1,x2) = y)
- If a 1-D table is to be used, it needs to be converted to a 2-D table (see file /samples/core/table_lookup/run01_table_lookup_sample.py)
- Extrapolation is not supported (provide a table grid large enough for your case)
- Class returns limit values in case of extrapolation attempt.
- Provided tables should be equally spaced.
"""
def __init__(self,
kernel_initializer = 'glorot_uniform',
kernel_regularizer=None,
kernel_constraint=None,
table_shape=(1,4,4,1),
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(TableInterpolation, self).__init__(**kwargs)
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.table_shape = table_shape
def build(self, input_shape, **kwargs):
self.grid = self.add_weight("grid",
shape = self.table_shape,
initializer = self.kernel_initializer,
dtype = self.dtype,
trainable = self.trainable,
**kwargs)
self.bounds = self.add_weight("bounds",
shape = [2,2],
initializer = self.kernel_initializer,
dtype = self.dtype,
trainable = self.trainable,
**kwargs)
self.built = True
def call(self, inputs):
self.grid = ops.convert_to_tensor(self.grid, dtype=self.dtype)
self.bounds = ops.convert_to_tensor(self.bounds,dtype=self.dtype)
inputs = ops.convert_to_tensor(inputs, dtype=self.dtype)
queryPoints_ind = ((cast(shape(self.grid)[1:3], dtype=self.dtype))-constant(1.0))*(inputs-self.bounds[0])/(self.bounds[1]-self.bounds[0])
if common_shapes.rank(inputs) == 2:
queryPoints_ind = expand_dims(queryPoints_ind,0)
output = interpolate(self.grid, queryPoints_ind)
if common_shapes.rank(inputs) == 2:
output = array_ops.reshape(output,(array_ops.shape(output)[1],) + (array_ops.shape(output)[2],))
return output
def compute_output_shape(self, input_shape):
aux_shape = tensor_shape.TensorShape((None,1))
return aux_shape[:-1].concatenate(1)
