Got most of the neural network stuff working. Some parameter updates to manage in NeuralNetworkSpecifications
youainti
5 years ago
parent
7b51044057
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import torch
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import combined as c
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"""
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This module holds the neural networks I am going to use to estimate
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the functions of interest.
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"""
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class LaunchFnEstimand(torch.nn.Module):
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"""
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This is used to estimate the launch function
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"""
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def __init__(self, state_tensor_size,layers_size,number_constellations):
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super().__init__()
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self.number_constellations = number_constellations
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self.layers_size = layers_size
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self.state_tensor_size = state_tensor_size
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#Layers
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self.linear_1 = torch.nn.Linear(in_features=state_tensor_size, out_features=layers_size)
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self.relu = torch.nn.ReLU()
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self.linear_3 = torch.nn.Linear(in_features=layers_size, out_features=layers_size)
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self.linear_5 = torch.nn.Linear(in_features=layers_size, out_features=number_constellations)
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def forward(self, input_values):
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intermediate_values = self.relu(input_values) #states should be positive anyway.
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intermediate_values = self.linear_1(intermediate_values)
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intermediate_values = self.linear_3(intermediate_values)
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intermediate_values = self.linear_5(intermediate_values)
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intermediate_values = self.relu(intermediate_values) #launches are always positive
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return intermediate_values
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class PartialDerivativesEstimand(torch.nn.Module):
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"""
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This is used to estimate the partial derivatives of the value functions
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"""
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def __init__(self,batch_size, number_constellations, number_states, layer_size=12):
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super().__init__()
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self.batch_size = batch_size
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self.number_constellations = number_constellations
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self.number_states = number_states
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self.layer_size = layer_size
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#preprocess (single linear layer in case there is anything that needs to happen to all states)
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self.preprocess = torch.nn.Sequential(
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torch.nn.ReLU() #cleanup as states must be positive
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,torch.nn.Linear(in_features = self.number_states, out_features=self.number_states)
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)
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#upscale to get the basic dimensionality correct. From (batch,State) to (batch, constellation, state). Includes a reshape
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self.upsample = lambda x: torch.nn.Upsample(scale_factor=self.number_constellations)(x).view(self.batch_size
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,self.number_constellations
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,self.number_states)
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#sequential steps
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self.sequential = torch.nn.Sequential(
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torch.nn.Linear(in_features=number_states, out_features=layer_size)
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#who knows if a convolution might help here.
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,torch.nn.Linear(in_features=layer_size, out_features=layer_size)
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,torch.nn.Linear(in_features=layer_size, out_features=layer_size)
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)
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#reduce the feature axis to match expected results
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self.feature_reduction = torch.nn.Linear(in_features=layer_size, out_features=number_states)
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def forward(self, input_values):
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#Note that the input values are just going to be the state variables
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#TODO:check that input values match the prepared dimension?
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#preprocess
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intermediate = self.preprocess(input_values)
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#upscale the input values
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intermediate = self.upsample(intermediate)
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#intermediate processing
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intermediate = self.sequential(intermediate)
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#reduce feature axis to match the expected number of partials
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intermediate = self.feature_reduction(intermediate)
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return intermediate
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class EstimandNN(torch.nn.Module):
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"""
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This neural network takes the current states as input values and returns both
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the partial derivatives of the value function and the launch function.
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"""
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def __init__(self, state_tensor_size,layers_size,number_constellations):
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super().__init__()
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#So, this next section constructs different layers within the NN
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#sinlge linear section
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pass
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#TODO:verify these are correct
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self.partials_estimator = PartialDerivativesEstimand(state_tensor_size,layers_size,number_constellations) #TODO
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self.launch_estimator = LaunchFnEstimand(state_tensor_size,layers_size,number_constellations)
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def forward(self, input_values):
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pass
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partials = self.partials_estimator(input_values)
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launch = self.launch_estimator(input_values)
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return c.EstimandInterface(partials,launch)
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@ -0,0 +1,203 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "least-cooling",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch"
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]
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},
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{
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"cell_type": "markdown",
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"id": "statistical-temperature",
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"metadata": {},
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"source": [
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"The purpose of this notebook is to allow me to investigate proper shaping of inputs.\n",
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"\n",
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"Typically pytorch chooses a tensor specification\n",
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"$$\n",
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"(N, .*)\n",
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"$$\n",
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"where $N$ is the batch size.\n",
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"For example a Convolutional NN layer expects\n",
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"$$\n",
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" NCHW\n",
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"$$\n",
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"for BatchSize,ChannelSize,Height,Width.\n",
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"On the other hand, Linear expects\n",
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"$$\n",
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" N.*H\n",
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"$$\n",
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"for BatchSize,any number of other dimensions, in_features\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "japanese-poultry",
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"metadata": {},
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"outputs": [],
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"source": [
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"class PartialDerivativesEstimand(torch.nn.Module):\n",
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" def __init__(self,batch_size, number_constellations, number_states,scale_factor=4, layer_size=12):\n",
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" \"\"\"\n",
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" \n",
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" \"\"\"\n",
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" super().__init__()\n",
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" self.batch_size = batch_size\n",
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" self.number_constellations = number_constellations\n",
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" self.number_states = number_states\n",
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" self.scale_factor = scale_factor\n",
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" self.layer_size = layer_size\n",
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" \n",
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" \n",
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" #preprocess (single linear layer in case there is anything that needs to happen to all states)\n",
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" self.preprocess = torch.nn.Sequential(\n",
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" torch.nn.ReLU() #cleanup as states must be positive\n",
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" ,torch.nn.Linear(in_features = self.number_states, out_features=self.number_states)\n",
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" )\n",
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" #upscale to get the basic dimensionality correct. From (batch,State) to (batch, constellation, state). Includes a reshape\n",
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" self.upsample = lambda x: torch.nn.Upsample(scale_factor=self.number_constellations)(x).view(self.batch_size\n",
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" ,self.number_constellations\n",
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" ,self.number_states)\n",
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" \n",
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" #sequential steps\n",
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" self.sequential = torch.nn.Sequential(\n",
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" torch.nn.Linear(in_features=number_states, out_features=layer_size)\n",
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" #who knows if a convolution might help here.\n",
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" ,torch.nn.Linear(in_features=layer_size, out_features=layer_size)\n",
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" ,torch.nn.Linear(in_features=layer_size, out_features=layer_size)\n",
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" )\n",
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"\n",
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" #reduce axis to match expectation\n",
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" self.feature_reduction = torch.nn.Linear(in_features=layer_size, out_features=number_states)\n",
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" \n",
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" def forward(self, input_values):\n",
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" #Note that the input values are just going to be the state variables\n",
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" #TODO:check that input values match the prepared dimension?\n",
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" \n",
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" #preprocess\n",
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" intermediate = self.preprocess(input_values)\n",
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" \n",
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" #upscale the input values\n",
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" intermediate = self.upsample(intermediate)\n",
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" \n",
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" #intermediate processing\n",
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" intermediate = self.sequential(intermediate)\n",
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" \n",
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" #reduce feature axis to match the expected number of partials\n",
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" intermediate = self.feature_reduction(intermediate)\n",
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" \n",
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" \n",
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" return intermediate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "communist-teach",
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"metadata": {},
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"outputs": [],
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"source": [
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"batch_size = 2\n",
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"constellations = 2\n",
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"number_states = constellations+1\n",
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"\n",
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"#initialize the NN\n",
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"a = PartialDerivativesEstimand(batch_size,constellations,number_states,scale_factor=2)\n",
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"\n",
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"#example state\n",
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"s = torch.rand(size=(batch_size,1,number_states))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "chemical-revolution",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[[0.9283, 0.9414, 0.3426]],\n",
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"\n",
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" [[0.1902, 0.0369, 0.4699]]])"
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]
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},
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"s"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "directed-lobby",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[[-0.1991, 0.1335, 0.2821],\n",
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" [-0.3549, 0.0213, 0.2322]],\n",
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"\n",
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" [[-0.1701, 0.1557, 0.2954],\n",
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" [-0.3017, 0.0690, 0.2419]]], grad_fn=<AddBackward0>)"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"a(s)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "placed-coating",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "advised-execution",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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