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Orbits/Code/SimplifiedApproach0.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ceramic-doctrine",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch.autograd.functional import jacobian\n",
"import itertools\n",
"import math\n",
"import abc\n",
"\n",
"class EconomicAgent(metaclass=abc.ABCMeta):\n",
" @abc.abstractmethod\n",
" def period_benefit(self,state,estimand_interface):\n",
" pass\n",
" @abc.abstractmethod\n",
" def _period_benefit(self):\n",
" pass\n",
" @abc.abstractmethod\n",
" def period_benefit_jacobian_wrt_states(self):\n",
" pass\n",
" @abc.abstractmethod\n",
" def _period_benefit_jacobian_wrt_states(self):\n",
" pass\n",
" @abc.abstractmethod\n",
" def period_benefit_jacobian_wrt_launches(self):\n",
" pass\n",
" @abc.abstractmethod\n",
" def _period_benefit_jacobian_wrt_launches(self):\n",
" pass\n",
"\n",
"class LinearProfit(EconomicAgent):\n",
" \"\"\"\n",
" The simplest type of profit function available.\n",
" \"\"\"\n",
" def __init__(self, constellation_number, discount_factor, benefit_weight, launch_cost, deorbit_cost=0):\n",
" #track which constellation this is.\n",
" self.constellation_number = constellation_number\n",
"\n",
" #parameters describing the agent's situation\n",
" self.discount_factor = discount_factor\n",
" self.benefit_weights = benefit_weight\n",
" self.launch_cost = launch_cost\n",
" self.deorbit_cost = deorbit_cost\n",
"\n",
" def __str__(self):\n",
" return \"LinearProfit\\n Benefit weights:\\t{}\\n launch cost:\\t{}\\n Deorbit cost:\\t{}\".format(self.benefit_weights, self.launch_cost, self.deorbit_cost)\n",
"\n",
" def period_benefit(self,state,estimand_interface):\n",
" return self._period_benefit(state.stocks, state.debris, estimand_interface.choices)\n",
" \n",
" def _period_benefit(self,stocks,debris,choice):\n",
" profits = self.benefit_weights @ stocks \\\n",
" - self.launch_cost * choice[self.constellation_number] #\\ \n",
" #- deorbit_cost @ deorbits[self.constellation_number]\n",
" return profits\n",
"\n",
" def period_benefit_jacobian_wrt_states(self, states, estimand_interface):\n",
" return self._period_benefit_jacobian_wrt_states(states.stocks, states.debris, estimand_interface.choices)\n",
"\n",
" def _period_benefit_jacobian_wrt_states(self, stocks, debris, launches):\n",
" jac = jacobian(self._period_benefit, (stocks,debris,launches))\n",
" return torch.cat((jac[0], jac[1]))\n",
" \n",
" def period_benefit_jacobian_wrt_launches(self, states, estimand_interface):\n",
" return self._period_benefit_jacobian_wrt_launches(states.stocks, states.debris, estimand_interface.choices)\n",
"\n",
" def _period_benefit_jacobian_wrt_launches(self,stocks,debris,launches):\n",
" jac = jacobian(self._period_benefit, (stocks,debris,launches))\n",
" return jac[2]\n",
"\n",
"class States():\n",
" \"\"\"\n",
" This is supposed to capture the state variables of the model, to create a common interface \n",
" when passing between functions.\n",
" \"\"\"\n",
" def __init__(self, stocks,debris):\n",
" self.stocks = stocks\n",
" self.debris = debris\n",
" \n",
"\n",
" def __str__(self):\n",
" return \"stocks\\t{} \\ndebris\\t {}\".format(self.stocks,self.debris)\n",
"\n",
" @property\n",
" def number_constellations(self):\n",
" return len(self.stocks)\n",
" @property\n",
" def number_debris_trackers(self):\n",
" return len(self.debris)\n",
"\n",
" \n",
"class EstimandInterface():\n",
" \"\"\"\n",
" This defines a clean interface for working with the estimand (i.e. thing we are trying to estimate).\n",
" In general, we are trying to estimate the choice variables and the partial derivatives of the value functions.\n",
" This \n",
"\n",
" This class wraps output for the neural network (or other estimand), allowing me to \n",
" - easily substitute various types of launch functions by having a common interface\n",
" - this eases testing\n",
" - check dimensionality etc without dealing with randomness\n",
" - again, easing testing\n",
" - reason more cleanly about the component pieces\n",
" - easing programming\n",
" - provide a clean interface to find constellation level launch decisions etc.\n",
"\n",
" It takes inputs of two general categories:\n",
" - the choice function results\n",
" - the partial derivatives of the value function\n",
" \"\"\"\n",
" def __init__(self, partials, choices, deorbits=None):\n",
" self.partials = partials\n",
" self.choices = choices\n",
" \n",
" @property\n",
" def number_constellations(self):\n",
" pass #fix this\n",
" return self.choices.shape[-1]\n",
" @property\n",
" def number_states(self):\n",
" pass #fix this\n",
" return self.partials.shape[-1] #This depends on the debris trackers technically.\n",
"\n",
" def choice_single(self, constellation):\n",
" #returns the launch decision for the constellation of interest\n",
" \n",
" filter_tensor = torch.zeros(self.number_constellations)\n",
" filter_tensor[constellation] = 1.0\n",
" \n",
" return self.choices @ filter_tensor\n",
" \n",
" def choice_vector(self, constellation):\n",
" #returns the launch decision for the constellation of interest as a vector\n",
" \n",
" filter_tensor = torch.zeros(self.number_constellations)\n",
" filter_tensor[constellation] = 1.0\n",
" \n",
" return self.choices * filter_tensor\n",
" \n",
" def partial_vector(self, constellation):\n",
" #returns the partials of the value function corresponding to the constellation of interest\n",
" \n",
" filter_tensor = torch.zeros(self.number_states)\n",
" filter_tensor[constellation] = 1.0\n",
" \n",
" return self.partials @ filter_tensor\n",
" \n",
" def partial_matrix(self, constellation):\n",
" #returns the partials of the value function corresponding to \n",
" #the constellation of interest as a matrix\n",
" \n",
" filter_tensor = torch.zeros(self.number_states)\n",
" filter_tensor[constellation] = 1.0\n",
" \n",
" return self.partials * filter_tensor\n",
" \n",
" def __str__(self):\n",
" #just a human readable descriptor\n",
" return \"Launch Decisions and Partial Derivativs of value function with\\n\\tlaunches\\n\\t\\t {}\\n\\tPartials\\n\\t\\t{}\".format(self.choices,self.partials)\n",
"\n",
"\n",
"class ChoiceFunction(torch.nn.Module):\n",
" \"\"\"\n",
" This is used to estimate the launch function\n",
" \"\"\"\n",
" def __init__(self\n",
" ,batch_size\n",
" ,number_states\n",
" ,number_choices\n",
" ,number_constellations\n",
" ,layer_size=12\n",
" ):\n",
" super().__init__()\n",
" \n",
" #preprocess\n",
" self.preprocess = torch.nn.Linear(in_features=number_states, out_features=layer_size)\n",
" \n",
" #upsample\n",
" self.upsample = lambda x: torch.nn.Upsample(scale_factor=number_constellations)(x).view(batch_size\n",
" ,number_constellations\n",
" ,layer_size)\n",
" \n",
" self.relu = torch.nn.ReLU() #used for coersion to the state space we care about.\n",
" \n",
" \n",
" #sequential steps\n",
" self.sequential = torch.nn.Sequential(\n",
" torch.nn.Linear(in_features=layer_size, out_features=layer_size)\n",
" #who knows if a convolution might help here.\n",
" ,torch.nn.Linear(in_features=layer_size, out_features=layer_size)\n",
" ,torch.nn.Linear(in_features=layer_size, out_features=layer_size)\n",
" )\n",
"\n",
" #reduce the feature axis to match expected results\n",
" self.feature_reduction = torch.nn.Linear(in_features=layer_size, out_features=number_choices)\n",
"\n",
" \n",
" def forward(self, input_values):\n",
" \n",
" intermediate_values = self.relu(input_values) #states should be positive anyway.\n",
" \n",
" intermediate_values = self.preprocess(intermediate_values)\n",
" intermediate_values = self.upsample(intermediate_values)\n",
" intermediate_values = self.sequential(intermediate_values)\n",
" intermediate_values = self.feature_reduction(intermediate_values)\n",
" \n",
" intermediate_values = self.relu(intermediate_values) #launches are always positive, this may need removed for other types of choices.\n",
" \n",
" return intermediate_values\n",
"\n",
"class PartialDerivativesOfValueEstimand(torch.nn.Module):\n",
" \"\"\"\n",
" This is used to estimate the partial derivatives of the value functions\n",
" \"\"\"\n",
" def __init__(self\n",
" ,batch_size\n",
" , number_constellations\n",
" , number_states\n",
" , layer_size=12):\n",
" super().__init__()\n",
" self.batch_size = batch_size #used for upscaling\n",
" self.number_constellations = number_constellations\n",
" self.number_states = number_states\n",
" self.layer_size = layer_size\n",
" \n",
" \n",
" #preprocess (single linear layer in case there is anything that needs to happen to all states)\n",
" self.preprocess = torch.nn.Sequential(\n",
" torch.nn.ReLU() #cleanup as states must be positive\n",
" ,torch.nn.Linear(in_features = self.number_states, out_features=self.number_states)\n",
" )\n",
" \n",
" #upsample to get the basic dimensionality correct. From (batch,State) to (batch, constellation, state). Includes a reshape\n",
" self.upsample = lambda x: torch.nn.Upsample(scale_factor=self.number_constellations)(x).view(self.batch_size\n",
" ,self.number_constellations\n",
" ,self.number_states)\n",
" \n",
" #sequential steps\n",
" self.sequential = torch.nn.Sequential(\n",
" torch.nn.Linear(in_features=number_states, out_features=layer_size)\n",
" #who knows if a convolution or other layer type might help here.\n",
" ,torch.nn.Linear(in_features=layer_size, out_features=layer_size)\n",
" ,torch.nn.Linear(in_features=layer_size, out_features=layer_size)\n",
" )\n",
"\n",
" #reduce the feature axis to match expected results\n",
" self.feature_reduction = torch.nn.Linear(in_features=layer_size, out_features=number_states)\n",
" \n",
" def forward(self, states):\n",
" #Note that the input values are just going to be the state variables\n",
" #TODO:check that input values match the prepared dimension?\n",
" \n",
" #preprocess\n",
" intermediate = self.preprocess(states)\n",
" \n",
" #upscale the input values\n",
" intermediate = self.upsample(intermediate)\n",
" \n",
" #intermediate processing\n",
" intermediate = self.sequential(intermediate)\n",
" \n",
" #reduce feature axis to match the expected number of partials\n",
" intermediate = self.feature_reduction(intermediate)\n",
" \n",
" \n",
" return intermediate\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "executive-royal",
"metadata": {},
"outputs": [],
"source": [
"class EstimandNN(torch.nn.Module):\n",
" \"\"\"\n",
" This neural network takes the current states as input values and returns both\n",
" the partial derivatives of the value function and the launch function.\n",
" \"\"\"\n",
" def __init__(self\n",
" ,batch_size\n",
" ,number_states\n",
" ,number_choices\n",
" ,number_constellations\n",
" ,layer_size=12\n",
" ):\n",
" super().__init__()\n",
" \n",
"\n",
" self.partials_estimator = PartialDerivativesOfValueEstimand(batch_size, number_constellations, number_states, layer_size)\n",
" self.launch_estimator = ChoiceFunction(batch_size, number_states, number_choices, number_constellations, layer_size)\n",
" \n",
" def forward(self, input_values):\n",
" pass\n",
" partials = self.partials_estimator(input_values)\n",
" launch = self.launch_estimator(input_values)\n",
" \n",
" return EstimandInterface(partials,launch)"
]
},
{
"cell_type": "markdown",
"id": "numerical-mexico",
"metadata": {},
"source": [
"# Testing\n",
"\n",
"Test if states can handle the dimensionality needed."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "packed-economics",
"metadata": {},
"outputs": [],
"source": [
"batch_size,states,choices = 5,3,1\n",
"constellations = states -1 #determined by debris tracking\n",
"max_start_state = 100\n",
"\n",
"stocks_and_debris = torch.randint(max_start_state,(batch_size,1,states),dtype=torch.float32)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "compliant-circle",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[[ 1., 30., 11.]],\n",
"\n",
" [[60., 74., 1.]],\n",
"\n",
" [[46., 33., 70.]],\n",
"\n",
" [[42., 29., 32.]],\n",
"\n",
" [[82., 72., 57.]]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stocks_and_debris"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "theoretical-spectrum",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Launch Decisions and Partial Derivativs of value function with\n",
"\tlaunches\n",
"\t\t tensor([[[0.0000],\n",
" [0.0000]],\n",
"\n",
" [[2.0907],\n",
" [0.1053]],\n",
"\n",
" [[2.9730],\n",
" [2.2000]],\n",
"\n",
" [[2.3975],\n",
" [1.2877]],\n",
"\n",
" [[4.2107],\n",
" [2.0752]]], grad_fn=<ReluBackward0>)\n",
"\tPartials\n",
"\t\ttensor([[[ 0.1939, 0.3954, 0.0730],\n",
" [-0.9428, 0.6145, -0.9247]],\n",
"\n",
" [[ 1.1686, 3.0170, 0.3393],\n",
" [-7.1474, 2.3495, -7.0566]],\n",
"\n",
" [[-2.0849, 3.0883, -3.3791],\n",
" [-0.6664, 0.0361, -2.2530]],\n",
"\n",
" [[-0.7117, 2.5474, -1.6458],\n",
" [-2.1937, 0.6897, -3.0382]],\n",
"\n",
" [[-1.0262, 4.5973, -2.6606],\n",
" [-5.4307, 1.4510, -6.6972]]], grad_fn=<AddBackward0>)\n"
]
}
],
"source": [
"print(a := enn.forward(stocks_and_debris))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "vulnerable-penalty",
"metadata": {},
"outputs": [],
"source": [
"def lossb(a):\n",
" #test loss function\n",
" return (a**2).sum()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "classified-estimate",
"metadata": {},
"outputs": [],
"source": [
"ch = ChoiceFunction(batch_size\n",
" ,states\n",
" ,choices\n",
" ,constellations\n",
" ,12)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "martial-premium",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(46.8100, grad_fn=<SumBackward0>)\n",
"tensor(82442.4219, grad_fn=<SumBackward0>)\n",
"tensor(0., grad_fn=<SumBackward0>)\n",
"tensor(0., grad_fn=<SumBackward0>)\n",
"tensor(0., grad_fn=<SumBackward0>)\n",
"tensor(0., grad_fn=<SumBackward0>)\n",
"tensor(0., grad_fn=<SumBackward0>)\n",
"tensor(0., grad_fn=<SumBackward0>)\n",
"tensor(0., grad_fn=<SumBackward0>)\n",
"tensor(0., grad_fn=<SumBackward0>)\n"
]
},
{
"data": {
"text/plain": [
"tensor([[[0.],\n",
" [0.]],\n",
"\n",
" [[0.],\n",
" [0.]],\n",
"\n",
" [[0.],\n",
" [0.]],\n",
"\n",
" [[0.],\n",
" [0.]],\n",
"\n",
" [[0.],\n",
" [0.]]], grad_fn=<ReluBackward0>)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimizer = torch.optim.SGD(ch.parameters(),lr=0.01)\n",
"\n",
"for i in range(10):\n",
" #training loop\n",
" optimizer.zero_grad()\n",
"\n",
" output = ch.forward(stocks_and_debris)\n",
"\n",
" l = lossb(output)\n",
"\n",
" l.backward()\n",
"\n",
" optimizer.step()\n",
"\n",
" print(l)\n",
" \n",
"\n",
"ch.forward(stocks_and_debris)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "corrected-jewelry",
"metadata": {},
"outputs": [],
"source": [
"def lossc(a):\n",
" #test loss function\n",
" return (a**2).sum()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "opened-figure",
"metadata": {},
"outputs": [],
"source": [
"pd = PartialDerivativesOfValueEstimand(\n",
" batch_size\n",
" ,constellations\n",
" ,states\n",
" ,12)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "chicken-inspector",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(1.9948e-06, grad_fn=<SumBackward0>)\n",
"tensor(1.7427e-05, grad_fn=<SumBackward0>)\n",
"tensor(5.7993e-06, grad_fn=<SumBackward0>)\n",
"tensor(2.9985e-06, grad_fn=<SumBackward0>)\n",
"tensor(6.5281e-06, grad_fn=<SumBackward0>)\n",
"tensor(7.8818e-06, grad_fn=<SumBackward0>)\n",
"tensor(4.4327e-06, grad_fn=<SumBackward0>)\n",
"tensor(1.1240e-06, grad_fn=<SumBackward0>)\n",
"tensor(1.2478e-06, grad_fn=<SumBackward0>)\n",
"tensor(3.5818e-06, grad_fn=<SumBackward0>)\n",
"tensor(4.3732e-06, grad_fn=<SumBackward0>)\n",
"tensor(2.7699e-06, grad_fn=<SumBackward0>)\n",
"tensor(8.9659e-07, grad_fn=<SumBackward0>)\n",
"tensor(5.7541e-07, grad_fn=<SumBackward0>)\n",
"tensor(1.5010e-06, grad_fn=<SumBackward0>)\n"
]
},
{
"data": {
"text/plain": [
"tensor([[[ 0.0002, -0.0002, -0.0003],\n",
" [ 0.0001, -0.0003, -0.0002]],\n",
"\n",
" [[ 0.0002, -0.0003, -0.0003],\n",
" [ 0.0003, -0.0004, -0.0002]],\n",
"\n",
" [[ 0.0002, -0.0003, -0.0003],\n",
" [ 0.0002, -0.0003, -0.0003]],\n",
"\n",
" [[ 0.0002, -0.0002, -0.0004],\n",
" [ 0.0003, -0.0003, -0.0003]],\n",
"\n",
" [[ 0.0003, -0.0003, -0.0002],\n",
" [ 0.0003, -0.0003, -0.0002]]], grad_fn=<AddBackward0>)"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimizer = torch.optim.Adam(pd.parameters(),lr=0.0001)\n",
"\n",
"for i in range(15):\n",
" #training loop\n",
" optimizer.zero_grad()\n",
"\n",
" output = pd.forward(stocks_and_debris)\n",
"\n",
" l = lossc(output)\n",
"\n",
" l.backward()\n",
"\n",
" optimizer.step()\n",
"\n",
" print(l)\n",
" \n",
"\n",
"pd.forward(stocks_and_debris)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "southwest-diamond",
"metadata": {},
"outputs": [],
"source": [
"def lossa(a):\n",
" #test loss function\n",
" return (a.choices**2).sum() + (a.partials**2).sum()"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "brave-treat",
"metadata": {},
"outputs": [],
"source": [
"enn = EstimandNN(batch_size\n",
" ,states\n",
" ,choices\n",
" ,constellations\n",
" ,12)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"id": "functional-render",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 tensor(112.1970, grad_fn=<AddBackward0>)\n",
"10 tensor(79.8152, grad_fn=<AddBackward0>)\n",
"20 tensor(55.6422, grad_fn=<AddBackward0>)\n",
"30 tensor(38.5636, grad_fn=<AddBackward0>)\n",
"40 tensor(26.9156, grad_fn=<AddBackward0>)\n",
"50 tensor(18.9986, grad_fn=<AddBackward0>)\n",
"60 tensor(13.6606, grad_fn=<AddBackward0>)\n",
"70 tensor(10.1881, grad_fn=<AddBackward0>)\n",
"80 tensor(8.0395, grad_fn=<AddBackward0>)\n",
"90 tensor(6.7618, grad_fn=<AddBackward0>)\n",
"100 tensor(6.0101, grad_fn=<AddBackward0>)\n",
"110 tensor(5.5517, grad_fn=<AddBackward0>)\n",
"120 tensor(5.2434, grad_fn=<AddBackward0>)\n",
"130 tensor(5.0054, grad_fn=<AddBackward0>)\n",
"140 tensor(4.7988, grad_fn=<AddBackward0>)\n",
"150 tensor(4.6069, grad_fn=<AddBackward0>)\n",
"160 tensor(4.4235, grad_fn=<AddBackward0>)\n",
"170 tensor(4.2468, grad_fn=<AddBackward0>)\n",
"180 tensor(4.0763, grad_fn=<AddBackward0>)\n",
"190 tensor(3.9117, grad_fn=<AddBackward0>)\n",
"200 tensor(3.7532, grad_fn=<AddBackward0>)\n",
"210 tensor(3.6005, grad_fn=<AddBackward0>)\n",
"220 tensor(3.4535, grad_fn=<AddBackward0>)\n",
"230 tensor(3.3121, grad_fn=<AddBackward0>)\n",
"240 tensor(3.1761, grad_fn=<AddBackward0>)\n",
"250 tensor(3.0454, grad_fn=<AddBackward0>)\n",
"260 tensor(2.9198, grad_fn=<AddBackward0>)\n",
"270 tensor(2.7991, grad_fn=<AddBackward0>)\n",
"280 tensor(2.6832, grad_fn=<AddBackward0>)\n",
"290 tensor(2.5720, grad_fn=<AddBackward0>)\n",
"300 tensor(2.4653, grad_fn=<AddBackward0>)\n",
"310 tensor(2.3629, grad_fn=<AddBackward0>)\n",
"320 tensor(2.2646, grad_fn=<AddBackward0>)\n",
"330 tensor(2.1704, grad_fn=<AddBackward0>)\n",
"340 tensor(2.0800, grad_fn=<AddBackward0>)\n",
"350 tensor(1.9933, grad_fn=<AddBackward0>)\n",
"360 tensor(1.9103, grad_fn=<AddBackward0>)\n",
"370 tensor(1.8306, grad_fn=<AddBackward0>)\n",
"380 tensor(1.7543, grad_fn=<AddBackward0>)\n",
"390 tensor(1.6812, grad_fn=<AddBackward0>)\n",
"400 tensor(1.6111, grad_fn=<AddBackward0>)\n",
"410 tensor(1.5440, grad_fn=<AddBackward0>)\n",
"420 tensor(1.4797, grad_fn=<AddBackward0>)\n",
"430 tensor(1.4180, grad_fn=<AddBackward0>)\n",
"440 tensor(1.3590, grad_fn=<AddBackward0>)\n",
"450 tensor(1.3025, grad_fn=<AddBackward0>)\n",
"460 tensor(1.2484, grad_fn=<AddBackward0>)\n",
"470 tensor(1.1965, grad_fn=<AddBackward0>)\n",
"480 tensor(1.1469, grad_fn=<AddBackward0>)\n",
"490 tensor(1.0994, grad_fn=<AddBackward0>)\n",
"500 tensor(1.0540, grad_fn=<AddBackward0>)\n",
"510 tensor(1.0104, grad_fn=<AddBackward0>)\n",
"520 tensor(0.9688, grad_fn=<AddBackward0>)\n",
"530 tensor(0.9290, grad_fn=<AddBackward0>)\n",
"540 tensor(0.8908, grad_fn=<AddBackward0>)\n",
"550 tensor(0.8544, grad_fn=<AddBackward0>)\n",
"560 tensor(0.8195, grad_fn=<AddBackward0>)\n",
"570 tensor(0.7861, grad_fn=<AddBackward0>)\n",
"580 tensor(0.7542, grad_fn=<AddBackward0>)\n",
"590 tensor(0.7237, grad_fn=<AddBackward0>)\n",
"600 tensor(0.6945, grad_fn=<AddBackward0>)\n",
"610 tensor(0.6667, grad_fn=<AddBackward0>)\n",
"620 tensor(0.6400, grad_fn=<AddBackward0>)\n",
"630 tensor(0.6146, grad_fn=<AddBackward0>)\n",
"640 tensor(0.5903, grad_fn=<AddBackward0>)\n",
"650 tensor(0.5671, grad_fn=<AddBackward0>)\n",
"660 tensor(0.5449, grad_fn=<AddBackward0>)\n",
"670 tensor(0.5237, grad_fn=<AddBackward0>)\n",
"680 tensor(0.5035, grad_fn=<AddBackward0>)\n",
"690 tensor(0.4842, grad_fn=<AddBackward0>)\n",
"700 tensor(0.4658, grad_fn=<AddBackward0>)\n",
"710 tensor(0.4482, grad_fn=<AddBackward0>)\n",
"720 tensor(0.4315, grad_fn=<AddBackward0>)\n",
"730 tensor(0.4155, grad_fn=<AddBackward0>)\n",
"740 tensor(0.4002, grad_fn=<AddBackward0>)\n",
"750 tensor(0.3857, grad_fn=<AddBackward0>)\n",
"760 tensor(0.3718, grad_fn=<AddBackward0>)\n",
"770 tensor(0.3586, grad_fn=<AddBackward0>)\n",
"780 tensor(0.3460, grad_fn=<AddBackward0>)\n",
"790 tensor(0.3340, grad_fn=<AddBackward0>)\n",
"800 tensor(0.3226, grad_fn=<AddBackward0>)\n",
"810 tensor(0.3117, grad_fn=<AddBackward0>)\n",
"820 tensor(0.3013, grad_fn=<AddBackward0>)\n",
"830 tensor(0.2914, grad_fn=<AddBackward0>)\n",
"840 tensor(0.2820, grad_fn=<AddBackward0>)\n",
"850 tensor(0.2730, grad_fn=<AddBackward0>)\n",
"860 tensor(0.2645, grad_fn=<AddBackward0>)\n",
"870 tensor(0.2564, grad_fn=<AddBackward0>)\n",
"880 tensor(0.2486, grad_fn=<AddBackward0>)\n",
"890 tensor(0.2413, grad_fn=<AddBackward0>)\n",
"900 tensor(0.2342, grad_fn=<AddBackward0>)\n",
"910 tensor(0.2276, grad_fn=<AddBackward0>)\n",
"920 tensor(0.2212, grad_fn=<AddBackward0>)\n",
"930 tensor(0.2151, grad_fn=<AddBackward0>)\n",
"940 tensor(0.2094, grad_fn=<AddBackward0>)\n",
"950 tensor(0.2039, grad_fn=<AddBackward0>)\n",
"960 tensor(0.1986, grad_fn=<AddBackward0>)\n",
"970 tensor(0.1936, grad_fn=<AddBackward0>)\n",
"980 tensor(0.1889, grad_fn=<AddBackward0>)\n",
"990 tensor(0.1844, grad_fn=<AddBackward0>)\n"
]
},
{
"data": {
"text/plain": [
"<__main__.EstimandInterface at 0x7f85609fce20>"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimizer = torch.optim.Adam(enn.parameters(),lr=0.0001) #note the use of enn in the optimizer\n",
"\n",
"for i in range(1000):\n",
" #training loop\n",
" optimizer.zero_grad()\n",
"\n",
" output = enn.forward(stocks_and_debris)\n",
"\n",
" l = lossa(output)\n",
"\n",
" l.backward()\n",
"\n",
" optimizer.step()\n",
"\n",
" if i%10==0:\n",
" print(i, l)\n",
" \n",
"\n",
"enn.forward(stocks_and_debris)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "voluntary-postage",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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