How to use _build_combination method in Slash

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ModelHelper.py

Source:ModelHelper.py Github

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...29 self._build_model_vq()30 elif self._settings.identifier == 'vanilla':31 self._build_model_vanilla()32 elif self._settings.identifier == 'combination':33 self._build_combination()34 self._input_tensor = self._misc.set_probabilities('model_checkpoint/vq_graph/p_s_m.mat')35 # elif self.settings.identifier == 'restore':36 # self._build_combination()37 def _build_model_vq(self):38 """39 Build your model for training. All of the architecture40 is defined here.41 :param scale_softmax: Scaling for the WTA-layer (Winner-Takes-All)42 :param codebook_size: Size of the codebook43 """44 # Start here to define your network45 # ------------------------------------------------------------------46 num_neurons = 51247 activation = tf.nn.relu48 regulizer = None49 if self._settings.l2_loss_reg:50 regulizer = tf.contrib.layers.l2_regularizer(scale=self._settings.scale_l2)51 with tf.variable_scope('base_network'):52 fc = tf.layers.dense(self.features, num_neurons, activation=activation, kernel_regularizer=regulizer)53 # fc1_bn = tf.layers.batch_normalization(fc1, training=self.train, center=False, scale=False)54 fc = tf.layers.batch_normalization(fc, training=self.train)55 # fc = tf.layers.dropout(fc, rate=self._settings.do_rate, training=self.train)56 fc = tf.layers.dense(fc, num_neurons, activation=activation, kernel_regularizer=regulizer)57 # fc2_bn = tf.layers.batch_normalization(fc2, training=self.train, center=False, scale=False)58 fc = tf.layers.batch_normalization(fc, training=self.train)59 # fc = tf.layers.dropout(fc, rate=self._settings.do_rate, training=self.train)60 fc = tf.layers.dense(fc, num_neurons, activation=activation, kernel_regularizer=regulizer)61 # fc3_bn = tf.layers.batch_normalization(fc3, training=self.train, center=False, scale=False)62 fc = tf.layers.batch_normalization(fc, training=self.train)63 # fc = tf.layers.dropout(fc, rate=self._settings.do_rate, training=self.train)64 #65 fc = tf.layers.dense(fc, num_neurons, activation=activation, kernel_regularizer=regulizer)66 # fc4_bn = tf.layers.batch_normalization(fc4, training=self.train, center=False, scale=False)67 fc = tf.layers.batch_normalization(fc, training=self.train)68 fc = tf.layers.dropout(fc, rate=self._settings.do_rate, training=self.train)69 #70 # fc = tf.layers.dense(fc, num_neurons, activation=activation)71 # # # fc3_bn = tf.layers.batch_normalization(fc3, training=self.train, center=False, scale=False)72 # fc = tf.layers.batch_normalization(fc, training=self.train)73 # fc = tf.layers.dropout(fc, rate=self._settings.do_rate, training=self.train)74 # # #75 # fc = tf.layers.dense(fc, num_neurons, activation=activation)76 # # # fc4_bn = tf.layers.batch_normalization(fc4, training=self.train, center=False, scale=False)77 # fc = tf.layers.batch_normalization(fc, training=self.train)78 # fc = tf.layers.dropout(fc, rate=self._settings.do_rate, training=self.train)79 # # WTA-layer starts here80 out = tf.layers.dense(fc, self._settings.codebook_size, activation=tf.nn.sigmoid,81 kernel_regularizer=regulizer)82 out_scaled = tf.scalar_mul(self._settings.scale_soft, out)83 # output without softmax84 self.logits = out_scaled85 # output with soft, be aware use a name 'nn_output' for the output node!86 self.inference = tf.nn.softmax(self.logits, name='nn_output')87 # Low rank matrix factorization88 # (c.f. LOW-RANK MATRIX FACTORIZATION FOR DEEP NEURAL NETWORK TRAINING WITH89 # HIGH-DIMENSIONAL OUTPUT TARGETS)90 # out_1 = tf.layers.dense(fc4_bn, 512, activation=None)91 # out = tf.layers.dense(out_1, self._settings.codebook_size, activation=None)92 # out_scaled = tf.scalar_mul(self._settings.scale_soft, out)93 # # output without softmax94 # self.logits = out_scaled95 # # output with soft, be aware use a name 'nn_output' for the output node!96 # self.inference = tf.nn.softmax(self.logits, name='nn_output')97 # ------------------------------------------------------------------98 # end of definition of network99 def _build_model_vanilla(self):100 num_neurons = 512101 with tf.variable_scope('vanilla_network'):102 fc1 = tf.layers.dense(self.features, num_neurons, activation=tf.nn.relu)103 fc1_bn = tf.layers.batch_normalization(fc1, training=self.train, center=False, scale=False)104 fc1_dropout = tf.layers.dropout(fc1_bn, rate=0.25, training=self.train)105 fc2 = tf.layers.dense(fc1_dropout, num_neurons, activation=tf.nn.relu)106 fc2_bn = tf.layers.batch_normalization(fc2, training=self.train, center=False, scale=False)107 fc2_dropout = tf.layers.dropout(fc2_bn, rate=0.25, training=self.train)108 #109 fc3 = tf.layers.dense(fc2_dropout, num_neurons, activation=tf.nn.relu)110 fc3_bn = tf.layers.batch_normalization(fc3, training=self.train, center=False, scale=False)111 fc3_dropout = tf.layers.dropout(fc3_bn, rate=0.25, training=self.train)112 fc4 = tf.layers.dense(fc3_dropout, num_neurons, activation=tf.nn.relu)113 fc4_bn = tf.layers.batch_normalization(fc4, training=self.train, center=False, scale=False)114 fc4_dropout = tf.layers.dropout(fc4_bn, rate=0.25, training=self.train)115 fc5 = tf.layers.dense(fc4_dropout, num_neurons, activation=tf.nn.relu)116 fc5_bn = tf.layers.batch_normalization(fc5, training=self.train, center=False, scale=False)117 fc5_dropout = tf.layers.dropout(fc5_bn, rate=0.25, training=self.train)118 self.logits = tf.layers.dense(fc5_dropout, self._settings.num_labels, activation=None)119 # output with soft, be aware use a name 'nn_output' for the output node!120 self.inference = tf.nn.softmax(self.logits, name='nn_output')121 def _build_combination(self):122 # we combine the nnvq and vanilla network123 num_neurons = 512124 # with tf.variable_scope('scaling_network'):125 # fc1_scale = tf.layers.dense(self.features, 1, activation=tf.nn.sigmoid)126 # self.scale = 35 * fc1_scale127 # # self.scale = tf.Print(self.scale, [tf.reduce_min(self.scale)])128 # first we build the nnvq network129 with tf.variable_scope('base_network'):130 fc1 = tf.layers.dense(self.features, num_neurons, activation=tf.nn.relu)131 fc1_bn = tf.layers.batch_normalization(fc1, training=self.train, center=False, scale=False)132 fc1_dropout = tf.layers.dropout(fc1_bn, rate=0.25, training=self.train)133 fc2 = tf.layers.dense(fc1_dropout, num_neurons, activation=tf.nn.relu)134 fc2_bn = tf.layers.batch_normalization(fc2, training=self.train, center=False, scale=False)135 fc2_dropout = tf.layers.dropout(fc2_bn, rate=0.25, training=self.train)...

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cartesian.py

Source:cartesian.py Github

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...22 return returned23 def check(self, iterator):24 names = list(self.sets)25 sets = [self.sets[name] for name in names]26 expected = sorted((self._build_combination(names, combination) for combination in itertools.product(*sets)), key=lambda d: sorted(d.items()))27 got = sorted(iterator, key=lambda d: sorted(d.items()))28 assert got == expected29 def _build_combination(self, names, combination):30 returned = {}31 for name, value in zip(names, combination):32 returned[name] = value33 for assign_source, assign_target in self._assigns:34 returned[assign_target] = returned[assign_source]35 return returned36class SetMaker(object):37 def __init__(self, cartesian, name):38 super(SetMaker, self).__init__()39 self.cartesian = cartesian40 self.name = name41 def make_set(self, size=3):42 assert self.name not in self.cartesian.sets43 returned = self.cartesian.sets[self.name] = ["{}{}".format(self.name, i) for i in range(size)]...

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