session.py

#

main.py | graph.py | tensor.py | ops.py | session.py

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import numpy as np

#

Session performs computation on a graph.

class Session(object):

#

Initializing a session with a graph and a state dictionary to hold tensor values.

    def __init__(self, graph):

#

        self.graph = graph
        self.state = {}

#

run_op takes as input an operation to run and a context to fetch pre-evaluted tensors.

    def run_op(self, op, context):

#

        args = [self.eval_tensor(tensor, context) for tensor in op.inputs]
        return op.compute(self, *args)

#

eval_tensor takes as input a tensor to evaluate and a context to fetch pre-evaluted tensors. If the tensor is not already in the context there are three possibilities for evaluating the tensor:

The tensor has an operation and is therefore the result of the operation that must be computed.
The tensor has an active state from another session run that can be fetched.
The tensor has an initial value from its instantiation that can be fetched and added to the state.

    def eval_tensor(self, tensor, context):

#

        if tensor not in context:
            if tensor.op is not None:
                context[tensor] = self.run_op(tensor.op, context)
            elif tensor in self.state and self.state[tensor] is not None:
                context[tensor] = self.state[tensor]
            elif tensor not in self.state and tensor.initial_value is not None:
                context[tensor] = self.state[tensor] = tensor.initial_value

        return context[tensor]

#

run takes a list of tensors to evaluate and a feed dictionary that can be used to override tensors.

    def run(self, tensors, feed_dict=None):

#

        context = {}

        if feed_dict:
            context.update(feed_dict)

        return [self.eval_tensor(tensor, context) for tensor in tensors]