Python tensorflow.variable_op_scope() Examples
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code examples of tensorflow.variable_op_scope().
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Example #1
Source File: fractal_block.py From FractalNet with MIT License | 6 votes |
def join(columns, coin): """Takes mean of the columns, applies drop path if `tflearn.get_training_mode()` is True. Args: columns: columns of fractal block. is_training: boolean in tensor form. Determines whether drop path should be used. coin: boolean in tensor form. Determines whether drop path is local or global. """ if len(columns)==1: return columns[0] with tf.variable_op_scope(columns, None, "Join"): columns = tf.convert_to_tensor(columns) columns = tf.cond(tflearn.get_training_mode(), lambda: drop_path(columns, coin), lambda: columns) out = tf.reduce_mean(columns, 0) return out
Example #2
Source File: ops.py From inception_v3 with Apache License 2.0 | 5 votes |
def repeat_op(repetitions, inputs, op, *args, **kwargs): """Build a sequential Tower starting from inputs by using an op repeatedly. It creates new scopes for each operation by increasing the counter. Example: given repeat_op(3, _, ops.conv2d, 64, [3, 3], scope='conv1') it will repeat the given op under the following variable_scopes: conv1/Conv conv1/Conv_1 conv1/Conv_2 Args: repetitions: number or repetitions. inputs: a tensor of size [batch_size, height, width, channels]. op: an operation. *args: args for the op. **kwargs: kwargs for the op. Returns: a tensor result of applying the operation op, num times. Raises: ValueError: if the op is unknown or wrong. """ scope = kwargs.pop('scope', None) with tf.variable_op_scope([inputs], scope, 'RepeatOp'): tower = inputs for _ in range(repetitions): tower = op(tower, *args, **kwargs) return tower
Example #3
Source File: ops.py From deeplearning-benchmark with Apache License 2.0 | 5 votes |
def repeat_op(repetitions, inputs, op, *args, **kwargs): """Build a sequential Tower starting from inputs by using an op repeatedly. It creates new scopes for each operation by increasing the counter. Example: given repeat_op(3, _, ops.conv2d, 64, [3, 3], scope='conv1') it will repeat the given op under the following variable_scopes: conv1/Conv conv1/Conv_1 conv1/Conv_2 Args: repetitions: number or repetitions. inputs: a tensor of size [batch_size, height, width, channels]. op: an operation. *args: args for the op. **kwargs: kwargs for the op. Returns: a tensor result of applying the operation op, num times. Raises: ValueError: if the op is unknown or wrong. """ scope = kwargs.pop('scope', None) with tf.variable_op_scope([inputs], scope, 'RepeatOp'): tower = inputs for _ in range(repetitions): tower = op(tower, *args, **kwargs) return tower
Example #4
Source File: ops.py From piecewisecrf with MIT License | 5 votes |
def repeat_op(repetitions, inputs, op, *args, **kwargs): """Build a sequential Tower starting from inputs by using an op repeatedly. It creates new scopes for each operation by increasing the counter. Example: given repeat_op(3, _, ops.conv2d, 64, [3, 3], scope='conv1') it will repeat the given op under the following variable_scopes: conv1/Conv conv1/Conv_1 conv1/Conv_2 Args: repetitions: number or repetitions. inputs: a tensor of size [batch_size, height, width, channels]. op: an operation. *args: args for the op. **kwargs: kwargs for the op. Returns: a tensor result of applying the operation op, num times. Raises: ValueError: if the op is unknown or wrong. """ scope = kwargs.pop('scope', None) with tf.variable_op_scope([inputs], scope, 'RepeatOp'): tower = inputs for _ in range(repetitions): tower = op(tower, *args, **kwargs) return tower
Example #5
Source File: ops.py From Action_Recognition_Zoo with MIT License | 5 votes |
def repeat_op(repetitions, inputs, op, *args, **kwargs): """Build a sequential Tower starting from inputs by using an op repeatedly. It creates new scopes for each operation by increasing the counter. Example: given repeat_op(3, _, ops.conv2d, 64, [3, 3], scope='conv1') it will repeat the given op under the following variable_scopes: conv1/Conv conv1/Conv_1 conv1/Conv_2 Args: repetitions: number or repetitions. inputs: a tensor of size [batch_size, height, width, channels]. op: an operation. *args: args for the op. **kwargs: kwargs for the op. Returns: a tensor result of applying the operation op, num times. Raises: ValueError: if the op is unknown or wrong. """ scope = kwargs.pop('scope', None) with tf.variable_op_scope([inputs], scope, 'RepeatOp'): tower = inputs for _ in range(repetitions): tower = op(tower, *args, **kwargs) return tower
Example #6
Source File: ops.py From ECO-pytorch with BSD 2-Clause "Simplified" License | 5 votes |
def repeat_op(repetitions, inputs, op, *args, **kwargs): """Build a sequential Tower starting from inputs by using an op repeatedly. It creates new scopes for each operation by increasing the counter. Example: given repeat_op(3, _, ops.conv2d, 64, [3, 3], scope='conv1') it will repeat the given op under the following variable_scopes: conv1/Conv conv1/Conv_1 conv1/Conv_2 Args: repetitions: number or repetitions. inputs: a tensor of size [batch_size, height, width, channels]. op: an operation. *args: args for the op. **kwargs: kwargs for the op. Returns: a tensor result of applying the operation op, num times. Raises: ValueError: if the op is unknown or wrong. """ scope = kwargs.pop('scope', None) with tf.variable_op_scope([inputs], scope, 'RepeatOp'): tower = inputs for _ in range(repetitions): tower = op(tower, *args, **kwargs) return tower
Example #7
Source File: fractal_block.py From FractalNet with MIT License | 5 votes |
def coin_flip(prob=.5): """Random boolean variable, with `prob` chance of being true. Used to choose between local and global drop path. Args: prob:float, probability of being True. """ with tf.variable_op_scope([],None,"CoinFlip"): coin = tf.random_uniform([1])[0]>prob return coin
Example #8
Source File: fractal_block.py From FractalNet with MIT License | 5 votes |
def drop_path(columns, coin): with tf.variable_op_scope([columns], None, "DropPath"): out = tf.cond(coin, lambda : drop_some(columns), lambda : random_column(columns)) return out
Example #9
Source File: ops.py From AI_Reader with Apache License 2.0 | 5 votes |
def repeat_op(repetitions, inputs, op, *args, **kwargs): """Build a sequential Tower starting from inputs by using an op repeatedly. It creates new scopes for each operation by increasing the counter. Example: given repeat_op(3, _, ops.conv2d, 64, [3, 3], scope='conv1') it will repeat the given op under the following variable_scopes: conv1/Conv conv1/Conv_1 conv1/Conv_2 Args: repetitions: number or repetitions. inputs: a tensor of size [batch_size, height, width, channels]. op: an operation. *args: args for the op. **kwargs: kwargs for the op. Returns: a tensor result of applying the operation op, num times. Raises: ValueError: if the op is unknown or wrong. """ scope = kwargs.pop('scope', None) with tf.variable_op_scope([inputs], scope, 'RepeatOp'): tower = inputs for _ in range(repetitions): tower = op(tower, *args, **kwargs) return tower
Example #10
Source File: ddpg_nets_dm.py From icnn with Apache License 2.0 | 5 votes |
def policy(obs, theta, name='policy'): with tf.variable_op_scope([obs], name, name): h0 = tf.identity(obs, name='h0-obs') h1 = tf.nn.relu(tf.matmul(h0, theta[0]) + theta[1], name='h1') h2 = tf.nn.relu(tf.matmul(h1, theta[2]) + theta[3], name='h2') h3 = tf.identity(tf.matmul(h2, theta[4]) + theta[5], name='h3') action = tf.nn.tanh(h3, name='h4-action') return action
Example #11
Source File: ddpg_nets_dm.py From icnn with Apache License 2.0 | 5 votes |
def qfunction(obs, act, theta, name="qfunction"): with tf.variable_op_scope([obs, act], name, name): h0 = tf.identity(obs, name='h0-obs') h0a = tf.identity(act, name='h0-act') h1 = tf.nn.relu(tf.matmul(h0, theta[0]) + theta[1], name='h1') h1a = tf.concat(1, [h1, act]) h2 = tf.nn.relu(tf.matmul(h1a, theta[2]) + theta[3], name='h2') qs = tf.matmul(h2, theta[4]) + theta[5] q = tf.squeeze(qs, [1], name='h3-q') return q
Example #12
Source File: fractal_block.py From FractalNet with MIT License | 4 votes |
def fractal_template(inputs, num_columns, block_fn, block_asc, joined=True, is_training=True, reuse=False, scope=None): """Template for making fractal blocks. Given a function and a corresponding arg_scope `fractal_template` will build a truncated fractal with `num_columns` columns. Args: inputs: a 4-D tensor `[batch_size, height, width, channels]`. num_columns: integer, the columns in the fractal. block_fn: function to be called within each fractal. block_as: A function that returns argscope for `block_fn`. joined: boolean, whether the output columns should be joined. reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. scope: Optional scope for `variable_scope`. """ def fractal_expand(inputs, num_columns, joined): '''Recursive Helper Function for making fractal''' with block_asc(): output = lambda cols: join(cols, coin) if joined else cols if num_columns == 1: return output([block_fn(inputs)]) left = block_fn(inputs) right = fractal_expand(inputs, num_columns-1, joined=True) right = fractal_expand(right, num_columns-1, joined=False) cols=[left]+right return output(cols) with tf.variable_op_scope([inputs], scope, 'Fractal', reuse=reuse) as scope: coin = coin_flip() net=fractal_expand(inputs, num_columns, joined) return net