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How to use kernel method in autotest
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multi_lead_branch_fusion.py
Source:multi_lead_branch_fusion.py 
1# -*- coding: utf-8 -*-2"""Multi Lead Branch Fusion3Automatically generated by Colaboratory.4Original file is located at5 https://colab.research.google.com/drive/1RfMexPUadW9T6txCWITOdRUBhsP-DOE86"""7input = Input(shape=(30000,6), dtype='float32', name= 'input')8x = Conv1D(12,kernel_size=3,strides=1, padding='same')(input)9x = LeakyReLU(alpha=0.3)(x)10x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)11x = LeakyReLU(alpha=0.3)(x)12x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)13x = LeakyReLU(alpha=0.3)(x)14x = Dropout(0.2)(x)15x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)16x = LeakyReLU(alpha=0.3)(x)17x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)18x = LeakyReLU(alpha=0.3)(x)19x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)20x = LeakyReLU(alpha=0.3)(x)21x = Dropout(0.2)(x)22x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)23x = LeakyReLU(alpha=0.3)(x)24x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)25x = LeakyReLU(alpha=0.3)(x)26x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)27x = LeakyReLU(alpha=0.3)(x)28x = Dropout(0.2)(x) 29x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)30x = LeakyReLU(alpha=0.3)(x)31x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)32x = LeakyReLU(alpha=0.3)(x)33x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)34x = LeakyReLU(alpha=0.3)(x)35x = Dropout(0.2)(x)36x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)37x = LeakyReLU(alpha=0.3)(x)38x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)39x = LeakyReLU(alpha=0.3)(x)40x = Conv1D(12,kernel_size=48, strides = 2, padding='same')(x)41x = LeakyReLU(alpha=0.3)(x)42x = Dropout(0.2)(x)43x1= Bidirectional(GRU(12, input_shape=(938,6),return_sequences=True,return_state=False),merge_mode='concat')(x)44x = LeakyReLU(alpha=0.3)(x1)45x = Dropout(0.2)(x)46x = AttentionWithContext()(x)47x = BatchNormalization()(x)48x = LeakyReLU(alpha=0.3)(x)49x = Dropout(0.2)(x)50x = Dense(7,activation='sigmoid')(x)51 52 #SECOND MODEL53x = Conv1D(12,kernel_size=3,strides=1, padding='same')(input)54x = LeakyReLU(alpha=0.3)(x)55x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)56x = LeakyReLU(alpha=0.3)(x)57x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)58x = LeakyReLU(alpha=0.3)(x)59x = Dropout(0.2)(x)60x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)61x = LeakyReLU(alpha=0.3)(x)62x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)63x = LeakyReLU(alpha=0.3)(x)64x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)65x = LeakyReLU(alpha=0.3)(x)66x = Dropout(0.5)(x)67x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)68x = LeakyReLU(alpha=0.3)(x)69x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)70x = LeakyReLU(alpha=0.3)(x)71x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)72x = LeakyReLU(alpha=0.3)(x)73x = Dropout(0.2)(x) 74x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)75x = LeakyReLU(alpha=0.3)(x)76x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)77x = LeakyReLU(alpha=0.3)(x)78x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)79x = LeakyReLU(alpha=0.3)(x)80x = Dropout(0.2)(x)81x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)82x = LeakyReLU(alpha=0.3)(x)83x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)84x = LeakyReLU(alpha=0.3)(x)85x = Conv1D(12,kernel_size=48, strides = 2, padding='same')(x)86x = LeakyReLU(alpha=0.3)(x)87x = Dropout(0.2)(x)88x2 = Bidirectional(GRU(12, input_shape=(938,6),return_sequences=True,return_state=False),merge_mode='concat')(x)89x = LeakyReLU(alpha=0.3)(x2)90x = Dropout(0.2)(x)91x = AttentionWithContext()(x)92x = BatchNormalization()(x)93x = LeakyReLU(alpha=0.3)(x)94x = Dropout(0.2)(x)95x = Dense(7,activation='sigmoid')(x)96 #THIRD MODEL97x = Conv1D(12,kernel_size=3,strides=1, padding='same')(input)98x = LeakyReLU(alpha=0.3)(x)99x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)100x = LeakyReLU(alpha=0.3)(x)101x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)102x = LeakyReLU(alpha=0.3)(x)103x = Dropout(0.2)(x)104x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)105x = LeakyReLU(alpha=0.3)(x)106x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)107x = LeakyReLU(alpha=0.3)(x)108x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)109x = LeakyReLU(alpha=0.3)(x)110x = Dropout(0.2)(x)111x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)112x = LeakyReLU(alpha=0.3)(x)113x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)114x = LeakyReLU(alpha=0.3)(x)115x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)116x = LeakyReLU(alpha=0.3)(x)117x = Dropout(0.2)(x) 118x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)119x = LeakyReLU(alpha=0.3)(x)120x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)121x = LeakyReLU(alpha=0.3)(x)122x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)123x = LeakyReLU(alpha=0.3)(x)124x = Dropout(0.2)(x)125x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)126x = LeakyReLU(alpha=0.3)(x)127x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)128x = LeakyReLU(alpha=0.3)(x)129x = Conv1D(12,kernel_size=48, strides = 2, padding='same')(x)130x = LeakyReLU(alpha=0.3)(x)131x= Dropout(0.2)(x)132x3= Bidirectional(GRU(12, input_shape=(938,6),return_sequences=True,return_state=False),merge_mode='concat')(x)133x = LeakyReLU(alpha=0.3)(x3)134x = Dropout(0.2)(x)135x = AttentionWithContext()(x)136x = BatchNormalization()(x)137x = LeakyReLU(alpha=0.3)(x)138x = Dropout(0.2)(x)139x = Dense(7,activation='sigmoid')(x)140 #MODEL 4141x = Conv1D(12,kernel_size=3,strides=1, padding='same')(input)142x = LeakyReLU(alpha=0.3)(x)143x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)144x = LeakyReLU(alpha=0.3)(x)145x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)146x = LeakyReLU(alpha=0.3)(x)147x = Dropout(0.2)(x)148x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)149x = LeakyReLU(alpha=0.3)(x)150x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)151x = LeakyReLU(alpha=0.3)(x)152x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)153x = LeakyReLU(alpha=0.3)(x)154x = Dropout(0.2)(x)155x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)156x = LeakyReLU(alpha=0.3)(x)157x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)158x = LeakyReLU(alpha=0.3)(x)159x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)160x = LeakyReLU(alpha=0.3)(x)161x = Dropout(0.2)(x) 162x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)163x = LeakyReLU(alpha=0.3)(x)164x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)165x = LeakyReLU(alpha=0.3)(x)166x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)167x = LeakyReLU(alpha=0.3)(x)168x = Dropout(0.2)(x)169x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)170x = LeakyReLU(alpha=0.3)(x)171x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)172x = LeakyReLU(alpha=0.3)(x)173x = Conv1D(12,kernel_size=48, strides = 2, padding='same')(x)174x = LeakyReLU(alpha=0.3)(x)175x= Dropout(0.2)(x)176x4= Bidirectional(GRU(12, input_shape=(938,6),return_sequences=True,return_state=False),merge_mode='concat')(x)177x = LeakyReLU(alpha=0.3)(x4)178x = Dropout(0.2)(x)179x = AttentionWithContext()(x)180x = BatchNormalization()(x)181x = LeakyReLU(alpha=0.3)(x)182x = Dropout(0.2)(x)183x = Dense(7,activation='sigmoid')(x)184 #MODEL 5185x = Conv1D(12,kernel_size=3,strides=1, padding='same')(input)186x = LeakyReLU(alpha=0.3)(x)187x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)188x = LeakyReLU(alpha=0.3)(x)189x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)190x = LeakyReLU(alpha=0.3)(x)191x = Dropout(0.2)(x)192x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)193x = LeakyReLU(alpha=0.3)(x)194x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)195x = LeakyReLU(alpha=0.3)(x)196x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)197x = LeakyReLU(alpha=0.3)(x)198x = Dropout(0.2)(x)199x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)200x = LeakyReLU(alpha=0.3)(x)201x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)202x = LeakyReLU(alpha=0.3)(x)203x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)204x = LeakyReLU(alpha=0.3)(x)205x = Dropout(0.2)(x) 206x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)207x = LeakyReLU(alpha=0.3)(x)208x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)209x = LeakyReLU(alpha=0.3)(x)210x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)211x = LeakyReLU(alpha=0.3)(x)212x = Dropout(0.2)(x)213x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)214x = LeakyReLU(alpha=0.3)(x)215x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)216x = LeakyReLU(alpha=0.3)(x)217x = Conv1D(12,kernel_size=48, strides = 2, padding='same')(x)218x = LeakyReLU(alpha=0.3)(x)219x = Dropout(0.2)(x)220x5= Bidirectional(GRU(12, input_shape=(938,6),return_sequences=True,return_state=False),merge_mode='concat')(x)221x = LeakyReLU(alpha=0.3)(x5)222x = Dropout(0.2)(x)223x = AttentionWithContext()(x)224x = BatchNormalization()(x)225x = LeakyReLU(alpha=0.3)(x)226x = Dropout(0.2)(x)227x= Dense(7,activation='sigmoid')(x)228 229 230 #MODEL 6231x = Conv1D(12,kernel_size=3,strides=1, padding='same')(input)232x = LeakyReLU(alpha=0.3)(x)233x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)234x = LeakyReLU(alpha=0.3)(x)235x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)236x = LeakyReLU(alpha=0.3)(x)237x = Dropout(0.2)(x)238x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)239x = LeakyReLU(alpha=0.3)(x)240x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)241x = LeakyReLU(alpha=0.3)(x)242x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)243x = LeakyReLU(alpha=0.3)(x)244x = Dropout(0.2)(x)245x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)246x = LeakyReLU(alpha=0.3)(x)247x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)248x = LeakyReLU(alpha=0.3)(x)249x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)250x = LeakyReLU(alpha=0.3)(x)251x = Dropout(0.2)(x) 252x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)253x = LeakyReLU(alpha=0.3)(x)254x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)255x = LeakyReLU(alpha=0.3)(x)256x = Conv1D(12,kernel_size=24, strides = 2, padding='same')(x)257x = LeakyReLU(alpha=0.3)(x)258x = Dropout(0.2)(x)259x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)260x = LeakyReLU(alpha=0.3)(x)261x = Conv1D(12,kernel_size=3,strides=1, padding='same')(x)262x = LeakyReLU(alpha=0.3)(x)263x = Conv1D(12,kernel_size=48, strides = 2, padding='same')(x)264x = LeakyReLU(alpha=0.3)(x)265x = Dropout(0.2)(x)266x6= Bidirectional(GRU(12, input_shape=(938,6),return_sequences=True,return_state=False),merge_mode='concat')(x)267x = LeakyReLU(alpha=0.3)(x6)268x = Dropout(0.2)(x)269x = AttentionWithContext()(x)270x = BatchNormalization()(x)271x = LeakyReLU(alpha=0.3)(x)272x = Dropout(0.2)(x)273x= Dense(7,activation='sigmoid')(x)274 #MERGE INPUT MODELS275z=concatenate([x1,x2,x3,x4,x5,x6])276print(z.shape)277#FINAL ATTENTIOON MODULE278x = AttentionWithContext()(z)279x = BatchNormalization()(x)280x = LeakyReLU(alpha=0.3)(x)281x = Dropout(0.2)(x)282x = Dense(50,activation='sigmoid')(x)283output = Dense(7,activation='sigmoid')(x)...
blocks_masked_conv2d_test.py
Source:blocks_masked_conv2d_test.py
1# Copyright 2017 The TensorFlow Authors All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Tests of the 2D masked convolution blocks."""16from __future__ import division17from __future__ import unicode_literals18import numpy as np19from six.moves import xrange20import tensorflow as tf21import blocks_masked_conv2d22class MaskedConv2DTest(tf.test.TestCase):23 def testRasterScanKernel(self):24 kernel_size = 525 input_depth = 126 output_depth = 127 kernel_shape = [kernel_size, kernel_size, input_depth, output_depth]28 # pylint: disable=bad-whitespace29 kernel_feed = [[ 1.0, 2.0, 3.0, 4.0, 5.0],30 [ 6.0, 7.0, 8.0, 9.0, 10.0],31 [11.0, 12.0, 13.0, 14.0, 15.0],32 [16.0, 17.0, 18.0, 19.0, 20.0],33 [21.0, 22.0, 23.0, 24.0, 25.0]]34 kernel_feed = np.reshape(kernel_feed, kernel_shape)35 kernel_expected = [[ 1.0, 2.0, 3.0, 4.0, 5.0],36 [ 6.0, 7.0, 8.0, 9.0, 10.0],37 [11.0, 12.0, 0.0, 0.0, 0.0],38 [ 0.0, 0.0, 0.0, 0.0, 0.0],39 [ 0.0, 0.0, 0.0, 0.0, 0.0]]40 kernel_expected = np.reshape(kernel_expected, kernel_shape)41 # pylint: enable=bad-whitespace42 init_kernel = lambda s, t: tf.constant(kernel_feed, dtype=t, shape=s)43 masked_conv2d = blocks_masked_conv2d.RasterScanConv2D(44 output_depth, [kernel_size] * 2, [1] * 2, 'SAME',45 initializer=init_kernel)46 x = tf.placeholder(dtype=tf.float32, shape=[10] * 3 + [input_depth])47 _ = masked_conv2d(x)48 with self.test_session():49 tf.global_variables_initializer().run()50 kernel_value = masked_conv2d._kernel.eval()51 self.assertAllEqual(kernel_expected, kernel_value)52 def testDepthOrderKernel(self):53 kernel_size = 154 input_depth = 755 output_depth = input_depth56 kernel_shape = [kernel_size, kernel_size, input_depth, output_depth]57 kernel_feed = np.ones(kernel_shape)58 x_shape = [5] * 3 + [input_depth]59 x_feed = np.ones(x_shape)60 y_expected = np.zeros(x_shape[0:3] + [output_depth])61 y_expected[:, :, :] = np.arange(output_depth)62 init_kernel = lambda s, t: tf.constant(kernel_feed, dtype=t, shape=s)63 masked_conv2d = blocks_masked_conv2d.DepthOrderConv2D(64 output_depth, [kernel_size] * 2, [1] * 2, 'SAME',65 strict_order=True,66 initializer=init_kernel)67 x = tf.placeholder(dtype=tf.float32, shape=x_shape)68 y = masked_conv2d(x)69 with self.test_session():70 tf.global_variables_initializer().run()71 y_value = y.eval(feed_dict={x: x_feed})72 self.assertAllEqual(y_expected, y_value)73 def testGroupRasterScanKernel(self):74 kernel_size = 375 input_depth = 476 input_group_size = 277 output_depth = 278 output_group_size = 179 kernel_shape = [kernel_size, kernel_size, input_depth, output_depth]80 kernel_feed = np.ones(shape=kernel_shape)81 height = 582 width = 583 x_shape = [1, height, width, input_depth]84 x_feed = np.ones(shape=x_shape)85 # pylint: disable=bad-whitespace86 y_expected = [87 [[ 0, 2], [ 4, 6], [ 4, 6], [ 4, 6], [ 4, 6]],88 [[ 8, 10], [16, 18], [16, 18], [16, 18], [12, 14]],89 [[ 8, 10], [16, 18], [16, 18], [16, 18], [12, 14]],90 [[ 8, 10], [16, 18], [16, 18], [16, 18], [12, 14]],91 [[ 8, 10], [16, 18], [16, 18], [16, 18], [12, 14]],92 ]93 y_expected = np.reshape(y_expected, [1, height, width, output_depth])94 # pylint: enable=bad-whitespace95 init_kernel = lambda s, t: tf.constant(kernel_feed, dtype=t, shape=s)96 masked_conv2d = blocks_masked_conv2d.GroupRasterScanConv2D(97 output_depth, [kernel_size] * 2, [1] * 2, 'SAME',98 strict_order=True,99 input_group_size=input_group_size,100 output_group_size=output_group_size,101 initializer=init_kernel)102 x = tf.placeholder(dtype=tf.float32, shape=x_shape)103 y = masked_conv2d(x)104 with self.test_session():105 tf.global_variables_initializer().run()106 y_value = y.eval(feed_dict={x: x_feed})107 self.assertAllEqual(y_expected, y_value)108 def testInFillingKernel(self):109 kernel_size = 5110 input_depth = 1111 output_depth = 1112 kernel_shape = [kernel_size, kernel_size, input_depth, output_depth]113 # pylint: disable=bad-whitespace114 kernel_feed = [[ 1.0, 2.0, 3.0, 4.0, 5.0],115 [ 6.0, 7.0, 8.0, 9.0, 10.0],116 [11.0, 12.0, 13.0, 14.0, 15.0],117 [16.0, 17.0, 18.0, 19.0, 20.0],118 [21.0, 22.0, 23.0, 24.0, 25.0]]119 kernel_feed = np.reshape(kernel_feed, kernel_shape)120 kernel_expected = [[ 1.0, 2.0, 3.0, 4.0, 5.0],121 [ 6.0, 7.0, 8.0, 9.0, 10.0],122 [11.0, 12.0, 0.0, 14.0, 15.0],123 [16.0, 17.0, 18.0, 19.0, 20.0],124 [21.0, 22.0, 23.0, 24.0, 25.0]]125 kernel_expected = np.reshape(kernel_expected, kernel_shape)126 # pylint: enable=bad-whitespace127 init_kernel = lambda s, t: tf.constant(kernel_feed, dtype=t, shape=s)128 masked_conv2d = blocks_masked_conv2d.InFillingConv2D(129 output_depth, [kernel_size] * 2, [1] * 2, 'SAME',130 initializer=init_kernel)131 x = tf.placeholder(dtype=tf.float32, shape=[10] * 3 + [input_depth])132 _ = masked_conv2d(x)133 with self.test_session():134 tf.global_variables_initializer().run()135 kernel_value = masked_conv2d._kernel.eval()136 self.assertAllEqual(kernel_expected, kernel_value)137 def testConv2DMaskedNumerics(self):138 kernel_size = 5139 input_shape = [1, 10, 10, 1]140 filter_shape = [kernel_size, kernel_size, 1, 1]141 strides = [1, 1, 1, 1]142 output_shape = [1, 10, 10, 1]143 conv = blocks_masked_conv2d.RasterScanConv2D(144 depth=filter_shape[-1],145 filter_size=filter_shape[0:2],146 strides=strides[1:3],147 padding='SAME',148 initializer=tf.constant_initializer(value=1.0))149 x = tf.placeholder(dtype=tf.float32, shape=input_shape)150 y = conv(x)151 x_feed = - np.ones(input_shape, dtype=float)152 y_expected = np.ones(output_shape, dtype=float)153 for i in xrange(input_shape[1]):154 for j in xrange(input_shape[2]):155 x_feed[0, i, j, 0] = 10 * (j + 1) + i156 v = 0157 ki_start = max(i - kernel_size // 2, 0)158 kj_start = max(j - kernel_size // 2, 0)159 kj_end = min(j + kernel_size // 2, input_shape[2] - 1)160 for ki in range(ki_start, i + 1):161 for kj in range(kj_start, kj_end + 1):162 if ki > i:163 continue164 if ki == i and kj >= j:165 continue166 v += 10 * (kj + 1) + ki167 y_expected[0, i, j, 0] = v168 with self.test_session():169 tf.global_variables_initializer().run()170 y_value = y.eval(feed_dict={x: x_feed})171 self.assertAllEqual(y_expected, y_value)172if __name__ == '__main__':...
models.py
Source:models.py
1from keras.layers import Dense, Input, LSTM, Dropout2from keras.models import Model, Sequential3from keras.initializers import Constant, RandomNormal, RandomUniform4from keras.optimizers import Adam5from keras.regularizers import l1, l26def create_model_stateless(seq_len, seq_dim, kernel_initializer=RandomUniform(), kernel_regularizer=None,7 hidden_units=50, hidden_layers=1, activation="tanh", regularization=0.01):8 x = Input(shape=(seq_len, seq_dim))9 if hidden_layers == 1:10 h = LSTM(hidden_units, return_sequences = False, activation=activation, kernel_initializer=kernel_initializer,11 kernel_regularizer=kernel_regularizer)(x)12 else:13 h = LSTM(hidden_units, return_sequences = True, activation=activation, kernel_initializer=kernel_initializer,14 kernel_regularizer=kernel_regularizer)(x)15 for i in range(1, hidden_layers):16 if i == hidden_layers - 1:17 h = LSTM(hidden_units, return_sequences = False, activation=activation, kernel_initializer=kernel_initializer,18 kernel_regularizer=kernel_regularizer)(h)19 else:20 h = LSTM(hidden_units, return_sequences = True, activation=activation, kernel_initializer=kernel_initializer,21 kernel_regularizer=kernel_regularizer)(h)22 y = Dense(seq_dim, activation = "linear", kernel_initializer=kernel_initializer, kernel_regularizer=None)(h)23 model = Model(x, y)24 model.compile(optimizer="adam", loss="mean_squared_error")25 return model26def create_model_stateful(batch_size, seq_len, seq_dim, kernel_initializer=RandomUniform(), kernel_regularizer=None,27 hidden_units=10, hidden_layers=1, activation="relu"):28 x = Input(batch_shape=(batch_size, seq_len, seq_dim))29 if hidden_layers ==1:30 h = LSTM(hidden_units, activation=activation, stateful=True, return_sequences=False,31 kernel_regularizer=kernel_regularizer, kernel_initializer=kernel_initializer)(x)32 else:33 h = LSTM(hidden_units, activation=activation, stateful=True, return_sequences=True,34 kernel_regularizer=kernel_regularizer, kernel_initializer=kernel_initializer)(x)35 for i in range(1, hidden_layers):36 if i == hidden_layers - 1:37 h = LSTM(hidden_units, activation=activation, stateful=True, return_sequences=False,38 kernel_regularizer=kernel_regularizer, kernel_initializer=kernel_initializer)(h)39 else:40 h = LSTM(hidden_units, activation=activation, stateful=True, return_sequences=True,41 kernel_regularizer=kernel_regularizer, kernel_initializer=kernel_initializer)(h)42 y = Dense(seq_dim, activation="linear", kernel_initializer=kernel_initializer)(h)43 model = Model(x, y)44 model.compile(optimizer="adam", loss="mean_squared_error")45 return model46def create_dropout_rnn(seq_len, seq_dim, kernel_initializer=RandomNormal(), kernel_regularizer=l2(0.0001), hidden_units=100,47 hidden_layers=1, activation="relu", dropout_recurrent=0.1, dropout_dense=0.1,48 dropout_input=0.1, dropout_lstm=0.1):49 x = Input(shape=(seq_len, seq_dim))50 h = Dropout(dropout_input)(x)51 h = LSTM(hidden_units, activation=activation, kernel_regularizer=kernel_regularizer, recurrent_regularizer=kernel_regularizer,52 recurrent_dropout=dropout_recurrent, dropout=dropout_lstm, bias_regularizer=kernel_regularizer,53 kernel_initializer=kernel_initializer, recurrent_initializer=kernel_initializer,54 bias_initializer=kernel_initializer)(h)55 h = Dropout(dropout_dense)(h)56 y = Dense(1, activation="linear", kernel_regularizer=kernel_regularizer, bias_regularizer=kernel_regularizer,57 kernel_initializer=kernel_initializer, bias_initializer=kernel_initializer)(h)58 model = Model(x, y)59 model.compile(optimizer="adam", loss="mean_squared_error")60 return model61def create_dropout_rnn_stateful(seq_len, seq_dim, kernel_initializer=RandomNormal(), kernel_regularizer=l2(0.0001), hidden_units=100,62 hidden_layers=1, activation="relu", dropout_recurrent=0.1, dropout_dense=0.1,63 dropout_input=0.1, dropout_lstm=0.1):64 x = Input(batch_shape=(1, seq_len, seq_dim))65 if hidden_layers == 1:66 h = Dropout(dropout_input)(x)67 h = LSTM(hidden_units, activation=activation, kernel_regularizer=kernel_regularizer, recurrent_regularizer=kernel_regularizer,68 recurrent_dropout=dropout_recurrent, dropout=dropout_lstm, bias_regularizer=kernel_regularizer, stateful=True,69 kernel_initializer=kernel_initializer, recurrent_initializer=kernel_initializer,70 bias_initializer=kernel_initializer, return_sequences=False)(h)71 for i in range(hidden_layers - 1):72 h = LSTM(hidden_units, activation=activation, kernel_regularizer=kernel_regularizer,73 recurrent_regularizer=kernel_regularizer,74 recurrent_dropout=dropout_recurrent, dropout=dropout_lstm, bias_regularizer=kernel_regularizer,75 stateful=True,76 kernel_initializer=kernel_initializer, recurrent_initializer=kernel_initializer,77 bias_initializer=kernel_initializer, return_sequences=True)(h)78 if hidden_layers > 1:79 h = LSTM(hidden_units, activation=activation, kernel_regularizer=kernel_regularizer,80 recurrent_regularizer=kernel_regularizer,81 recurrent_dropout=dropout_recurrent, dropout=dropout_lstm, bias_regularizer=kernel_regularizer,82 stateful=True,83 kernel_initializer=kernel_initializer, recurrent_initializer=kernel_initializer,84 bias_initializer=kernel_initializer, return_sequences=False)(h)85 h = Dropout(dropout_dense)(h)86 y = Dense(1, activation="linear", kernel_regularizer=kernel_regularizer, bias_regularizer=kernel_regularizer,87 kernel_initializer=kernel_initializer, bias_initializer=kernel_initializer)(h)88 model = Model(x, y)89 model.compile(optimizer="adam", loss="mean_squared_error")90 return model91def reinstantiate_model(config, weights):92 model = Model.from_config(config)93 model.set_weights(weights)94 return model95# class CustomLSTM(LSTM):96# def call(self, inputs, mask=None, training=None, initial_state=None):97# self.cell._generate_dropout_mask(inputs, training=training)98# self.cell._generate_recurrent_dropout_mask(inputs, training=training)99# return RNN.call(inputs, mask=mask, training=training,...
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