Python sonnet.Conv1D() Examples
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code examples of sonnet.Conv1D().
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Example #1
Source File: more_local_weight_update.py From Gun-Detector with Apache License 2.0 | 6 votes |
def _build(self, x): # x is [units, bs, 1] net = tf.transpose(x, [1, 0, 2]) # now [bs x units x 1] channels = x.shape.as_list()[2] mod = snt.Conv1D(output_channels=channels, kernel_shape=[3]) net = mod(net) net = snt.BatchNorm(axis=[0, 1])(net, is_training=False) net = tf.nn.relu(net) mod = snt.Conv1D(output_channels=channels, kernel_shape=[3]) net = mod(net) net = snt.BatchNorm(axis=[0, 1])(net, is_training=False) net = tf.nn.relu(net) to_concat = tf.transpose(net, [1, 0, 2]) if self.add: return x + to_concat else: return tf.concat([x, to_concat], 2)
Example #2
Source File: bounds_test.py From interval-bound-propagation with Apache License 2.0 | 6 votes |
def testConv1dIntervalBounds(self): m = snt.Conv1D( output_channels=1, kernel_shape=2, padding='VALID', stride=1, use_bias=True, initializers={ 'w': tf.constant_initializer(1.), 'b': tf.constant_initializer(2.), }) z = tf.constant([3, 4], dtype=tf.float32) z = tf.reshape(z, [1, 2, 1]) m(z) # Connect to create weights. m = ibp.LinearConv1dWrapper(m) input_bounds = ibp.IntervalBounds(z - 1., z + 1.) output_bounds = m.propagate_bounds(input_bounds) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) l, u = sess.run([output_bounds.lower, output_bounds.upper]) l = l.item() u = u.item() self.assertAlmostEqual(7., l) self.assertAlmostEqual(11., u)
Example #3
Source File: more_local_weight_update.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
def _build(self, x): # x is [units, bs, 1] net = tf.transpose(x, [1, 0, 2]) # now [bs x units x 1] channels = x.shape.as_list()[2] mod = snt.Conv1D(output_channels=channels, kernel_shape=[3]) net = mod(net) net = snt.BatchNorm(axis=[0, 1])(net, is_training=False) net = tf.nn.relu(net) mod = snt.Conv1D(output_channels=channels, kernel_shape=[3]) net = mod(net) net = snt.BatchNorm(axis=[0, 1])(net, is_training=False) net = tf.nn.relu(net) to_concat = tf.transpose(net, [1, 0, 2]) if self.add: return x + to_concat else: return tf.concat([x, to_concat], 2)
Example #4
Source File: more_local_weight_update.py From models with Apache License 2.0 | 6 votes |
def _build(self, x): # x is [units, bs, 1] net = tf.transpose(x, [1, 0, 2]) # now [bs x units x 1] channels = x.shape.as_list()[2] mod = snt.Conv1D(output_channels=channels, kernel_shape=[3]) net = mod(net) net = snt.BatchNorm(axis=[0, 1])(net, is_training=False) net = tf.nn.relu(net) mod = snt.Conv1D(output_channels=channels, kernel_shape=[3]) net = mod(net) net = snt.BatchNorm(axis=[0, 1])(net, is_training=False) net = tf.nn.relu(net) to_concat = tf.transpose(net, [1, 0, 2]) if self.add: return x + to_concat else: return tf.concat([x, to_concat], 2)
Example #5
Source File: more_local_weight_update.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
def _build(self, x): # x is [units, bs, 1] net = tf.transpose(x, [1, 0, 2]) # now [bs x units x 1] channels = x.shape.as_list()[2] mod = snt.Conv1D(output_channels=channels, kernel_shape=[3]) net = mod(net) net = snt.BatchNorm(axis=[0, 1])(net, is_training=False) net = tf.nn.relu(net) mod = snt.Conv1D(output_channels=channels, kernel_shape=[3]) net = mod(net) net = snt.BatchNorm(axis=[0, 1])(net, is_training=False) net = tf.nn.relu(net) to_concat = tf.transpose(net, [1, 0, 2]) if self.add: return x + to_concat else: return tf.concat([x, to_concat], 2)
Example #6
Source File: model.py From interval-bound-propagation with Apache License 2.0 | 5 votes |
def _inputs_for_observed_module(self, subgraph): """Extracts input tensors from a connected Sonnet module. This default implementation supports common layer types, but should be overridden if custom layer types are to be supported. Args: subgraph: `snt.ConnectedSubGraph` specifying the Sonnet module being connected, and its inputs and outputs. Returns: List of input tensors, or None if not a supported Sonnet module. """ m = subgraph.module # Only support a few operations for now. if not (isinstance(m, snt.BatchReshape) or isinstance(m, snt.Linear) or isinstance(m, snt.Conv1D) or isinstance(m, snt.Conv2D) or isinstance(m, snt.BatchNorm) or isinstance(m, layers.ImageNorm)): return None if isinstance(m, snt.BatchNorm): return subgraph.inputs['input_batch'], else: return subgraph.inputs['inputs'],
Example #7
Source File: model.py From interval-bound-propagation with Apache License 2.0 | 5 votes |
def _wrapper_for_observed_module(self, subgraph): """Creates a wrapper for a connected Sonnet module. This default implementation supports common layer types, but should be overridden if custom layer types are to be supported. Args: subgraph: `snt.ConnectedSubGraph` specifying the Sonnet module being connected, and its inputs and outputs. Returns: `ibp.VerifiableWrapper` for the Sonnet module. """ m = subgraph.module if isinstance(m, snt.BatchReshape): shape = subgraph.outputs.get_shape()[1:].as_list() return verifiable_wrapper.BatchReshapeWrapper(m, shape) elif isinstance(m, snt.Linear): return verifiable_wrapper.LinearFCWrapper(m) elif isinstance(m, snt.Conv1D): return verifiable_wrapper.LinearConv1dWrapper(m) elif isinstance(m, snt.Conv2D): return verifiable_wrapper.LinearConv2dWrapper(m) elif isinstance(m, layers.ImageNorm): return verifiable_wrapper.ImageNormWrapper(m) else: assert isinstance(m, snt.BatchNorm) return verifiable_wrapper.BatchNormWrapper(m)
Example #8
Source File: verifiable_wrapper.py From interval-bound-propagation with Apache License 2.0 | 5 votes |
def __init__(self, module): if not isinstance(module, snt.Conv1D): raise ValueError('Cannot wrap {} with a LinearConv1dWrapper.'.format( module)) super(LinearConv1dWrapper, self).__init__(module)
Example #9
Source File: fastlin_test.py From interval-bound-propagation with Apache License 2.0 | 5 votes |
def testConv1dSymbolicBounds(self): m = snt.Conv1D( output_channels=1, kernel_shape=(2), padding='VALID', stride=1, use_bias=True, initializers={ 'w': tf.constant_initializer(1.), 'b': tf.constant_initializer(3.), }) z = tf.constant([3, 4], dtype=tf.float32) z = tf.reshape(z, [1, 2, 1]) m(z) # Connect to create weights. m = ibp.LinearConv1dWrapper(m) input_bounds = ibp.IntervalBounds(z - 1., z + 1.) input_bounds = ibp.SymbolicBounds.convert(input_bounds) output_bounds = m.propagate_bounds(input_bounds) output_bounds = ibp.IntervalBounds.convert(output_bounds) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) l, u = sess.run([output_bounds.lower, output_bounds.upper]) l = l.item() u = u.item() self.assertAlmostEqual(8., l) self.assertAlmostEqual(12., u)
Example #10
Source File: more_local_weight_update.py From Gun-Detector with Apache License 2.0 | 4 votes |
def compute_top_delta(self, z): """ parameterization of topD. This converts the top level activation to an error signal. Args: z: tf.Tensor batch of final layer post activations Returns delta: tf.Tensor the error signal """ s_idx = 0 with tf.variable_scope('compute_top_delta'), tf.device(self.remote_device): # typically this takes [BS, length, input_channels], # We are applying this such that we convolve over the batch dimension. act = tf.expand_dims(tf.transpose(z, [1, 0]), 2) # [channels, BS, 1] mod = snt.Conv1D(output_channels=self.top_delta_size, kernel_shape=[5]) act = mod(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) bs = act.shape.as_list()[0] act = tf.transpose(act, [2, 1, 0]) act = snt.Conv1D(output_channels=bs, kernel_shape=[3])(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) act = snt.Conv1D(output_channels=bs, kernel_shape=[3])(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) act = tf.transpose(act, [2, 1, 0]) prev_act = act for i in range(self.top_delta_layers): mod = snt.Conv1D(output_channels=self.top_delta_size, kernel_shape=[3]) act = mod(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) prev_act = act mod = snt.Conv1D(output_channels=self.delta_dim, kernel_shape=[3]) act = mod(act) # [bs, feature_channels, delta_channels] act = tf.transpose(act, [1, 0, 2]) return act
Example #11
Source File: more_local_weight_update.py From g-tensorflow-models with Apache License 2.0 | 4 votes |
def compute_top_delta(self, z): """ parameterization of topD. This converts the top level activation to an error signal. Args: z: tf.Tensor batch of final layer post activations Returns delta: tf.Tensor the error signal """ s_idx = 0 with tf.variable_scope('compute_top_delta'), tf.device(self.remote_device): # typically this takes [BS, length, input_channels], # We are applying this such that we convolve over the batch dimension. act = tf.expand_dims(tf.transpose(z, [1, 0]), 2) # [channels, BS, 1] mod = snt.Conv1D(output_channels=self.top_delta_size, kernel_shape=[5]) act = mod(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) bs = act.shape.as_list()[0] act = tf.transpose(act, [2, 1, 0]) act = snt.Conv1D(output_channels=bs, kernel_shape=[3])(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) act = snt.Conv1D(output_channels=bs, kernel_shape=[3])(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) act = tf.transpose(act, [2, 1, 0]) prev_act = act for i in range(self.top_delta_layers): mod = snt.Conv1D(output_channels=self.top_delta_size, kernel_shape=[3]) act = mod(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) prev_act = act mod = snt.Conv1D(output_channels=self.delta_dim, kernel_shape=[3]) act = mod(act) # [bs, feature_channels, delta_channels] act = tf.transpose(act, [1, 0, 2]) return act
Example #12
Source File: more_local_weight_update.py From models with Apache License 2.0 | 4 votes |
def compute_top_delta(self, z): """ parameterization of topD. This converts the top level activation to an error signal. Args: z: tf.Tensor batch of final layer post activations Returns delta: tf.Tensor the error signal """ s_idx = 0 with tf.variable_scope('compute_top_delta'), tf.device(self.remote_device): # typically this takes [BS, length, input_channels], # We are applying this such that we convolve over the batch dimension. act = tf.expand_dims(tf.transpose(z, [1, 0]), 2) # [channels, BS, 1] mod = snt.Conv1D(output_channels=self.top_delta_size, kernel_shape=[5]) act = mod(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) bs = act.shape.as_list()[0] act = tf.transpose(act, [2, 1, 0]) act = snt.Conv1D(output_channels=bs, kernel_shape=[3])(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) act = snt.Conv1D(output_channels=bs, kernel_shape=[3])(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) act = tf.transpose(act, [2, 1, 0]) prev_act = act for i in range(self.top_delta_layers): mod = snt.Conv1D(output_channels=self.top_delta_size, kernel_shape=[3]) act = mod(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) prev_act = act mod = snt.Conv1D(output_channels=self.delta_dim, kernel_shape=[3]) act = mod(act) # [bs, feature_channels, delta_channels] act = tf.transpose(act, [1, 0, 2]) return act
Example #13
Source File: more_local_weight_update.py From multilabel-image-classification-tensorflow with MIT License | 4 votes |
def compute_top_delta(self, z): """ parameterization of topD. This converts the top level activation to an error signal. Args: z: tf.Tensor batch of final layer post activations Returns delta: tf.Tensor the error signal """ s_idx = 0 with tf.variable_scope('compute_top_delta'), tf.device(self.remote_device): # typically this takes [BS, length, input_channels], # We are applying this such that we convolve over the batch dimension. act = tf.expand_dims(tf.transpose(z, [1, 0]), 2) # [channels, BS, 1] mod = snt.Conv1D(output_channels=self.top_delta_size, kernel_shape=[5]) act = mod(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) bs = act.shape.as_list()[0] act = tf.transpose(act, [2, 1, 0]) act = snt.Conv1D(output_channels=bs, kernel_shape=[3])(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) act = snt.Conv1D(output_channels=bs, kernel_shape=[3])(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) act = tf.transpose(act, [2, 1, 0]) prev_act = act for i in range(self.top_delta_layers): mod = snt.Conv1D(output_channels=self.top_delta_size, kernel_shape=[3]) act = mod(act) act = snt.BatchNorm(axis=[0, 1])(act, is_training=False) act = tf.nn.relu(act) prev_act = act mod = snt.Conv1D(output_channels=self.delta_dim, kernel_shape=[3]) act = mod(act) # [bs, feature_channels, delta_channels] act = tf.transpose(act, [1, 0, 2]) return act