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203 lines
6.5 KiB
Plaintext
203 lines
6.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "heavy-turkish",
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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": "code",
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"execution_count": 4,
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"id": "ordered-disease",
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"metadata": {},
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"outputs": [],
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"source": [
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"class LinearNet(torch.nn.Module):\n",
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" def __init__(self, input_size,output_size,layers_size):\n",
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" super().__init__()\n",
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" \n",
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" #So, this next section constructs different layers within the NN\n",
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" #sinlge linear section\n",
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" self.linear_step1 = torch.nn.Linear(input_size,layers_size)\n",
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" #single linear section\n",
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" self.linear_step2 = torch.nn.Linear(layers_size,output_size)\n",
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" \n",
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" def forward(self, input_values):\n",
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" intermediate_values = self.linear_step1(input_values)\n",
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" out_values = self.linear_step2(intermediate_values)\n",
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" \n",
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" return out_values"
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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": "quiet-syria",
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"metadata": {},
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"outputs": [],
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"source": [
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"class LinearNet2(torch.nn.Module):\n",
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" def __init__(self, input_size,output_size,layers_size):\n",
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" super().__init__()\n",
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" \n",
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" #combined together into a set of sequential workers\n",
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" self.sequential_layers = torch.nn.Sequential(\n",
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" torch.nn.Linear(input_size,layers_size)\n",
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" torch.nn.Linear(layers_size,output_size)\n",
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" )\n",
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" \n",
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" def forward(self, input_values):\n",
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" return self.sequential_layers(input_values)"
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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": 6,
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"id": "fleet-letter",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = LinearNet(input_size = 5, output_size=5, layers_size=15)"
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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": "provincial-crime",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"LinearNet(\n",
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" (linear_step1): Linear(in_features=5, out_features=15, bias=True)\n",
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" (linear_step2): Linear(in_features=15, out_features=5, bias=True)\n",
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")\n"
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]
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}
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],
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"source": [
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"print(model)"
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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": 12,
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"id": "particular-response",
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"metadata": {},
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"outputs": [],
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"source": [
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"data_in = torch.tensor([1.5,2,3,4,5])"
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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": 11,
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"id": "passing-termination",
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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([0.3597, 0.7032, 0.0924, 0.7974, 3.1524], grad_fn=<AddBackward0>)"
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]
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},
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"execution_count": 11,
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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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"model.forward(data_in)"
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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": 13,
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"id": "public-speaking",
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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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"OrderedDict([('linear_step1.weight',\n",
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" tensor([[ 0.2498, -0.2177, 0.2323, 0.3493, -0.2541],\n",
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" [-0.2169, 0.0535, -0.1223, 0.0237, -0.0184],\n",
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" [-0.1554, -0.0134, 0.2918, 0.3542, 0.3464],\n",
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" [-0.2378, 0.3828, -0.3026, -0.1545, -0.1484],\n",
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" [ 0.3282, -0.1492, 0.3551, -0.0447, -0.3294],\n",
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" [ 0.2789, -0.1546, 0.2821, 0.0136, 0.4210],\n",
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" [ 0.2911, -0.2191, 0.0493, 0.1006, 0.0470],\n",
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" [-0.2269, 0.1705, 0.1198, 0.4040, 0.2512],\n",
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" [-0.2696, -0.4259, 0.4229, 0.1412, 0.3553],\n",
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" [ 0.0293, 0.4044, 0.3961, -0.3992, 0.2586],\n",
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" [-0.3101, -0.0327, 0.1832, 0.0295, -0.3185],\n",
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" [ 0.0637, -0.0770, 0.2297, -0.1567, 0.4379],\n",
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" [-0.0540, -0.1769, 0.3407, 0.1942, 0.3494],\n",
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" [-0.3609, -0.3536, 0.2491, -0.0490, -0.1199],\n",
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" [ 0.2946, -0.0782, -0.0580, 0.2313, -0.0696]])),\n",
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" ('linear_step1.bias',\n",
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" tensor([-0.4207, -0.1624, 0.0212, -0.0988, -0.2106, 0.2991, -0.3496, -0.1799,\n",
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" -0.4257, -0.3384, -0.0020, 0.1267, 0.0252, 0.0037, 0.1784])),\n",
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" ('linear_step2.weight',\n",
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" tensor([[ 0.1404, -0.1424, 0.1518, -0.1080, 0.1269, -0.2030, -0.0533, -0.2240,\n",
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" 0.0364, -0.0393, 0.1619, 0.1242, 0.0731, -0.1545, 0.2024],\n",
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" [-0.2529, 0.0578, 0.1629, -0.0352, -0.2128, 0.0429, 0.0261, 0.2264,\n",
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" -0.0470, 0.0277, 0.0272, -0.1074, -0.1334, 0.0792, -0.0173],\n",
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" [ 0.0459, 0.2224, -0.2272, 0.0123, -0.0676, 0.2378, 0.2166, -0.0981,\n",
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" 0.1010, -0.1593, -0.2422, -0.1253, 0.0899, -0.0760, -0.0816],\n",
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" [ 0.1763, 0.2344, 0.0591, -0.2299, 0.1116, 0.0604, 0.2032, 0.1298,\n",
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" 0.0509, 0.2581, 0.2425, -0.0920, 0.0098, 0.1353, -0.2110],\n",
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" [ 0.0726, -0.1959, 0.2114, -0.0732, -0.1089, 0.0836, -0.1061, 0.1640,\n",
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" 0.1221, 0.0281, -0.2401, 0.1108, 0.1354, 0.1903, -0.1006]])),\n",
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" ('linear_step2.bias',\n",
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" tensor([0.2462, 0.0098, 0.1239, 0.0689, 0.2404]))])"
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]
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},
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"execution_count": 13,
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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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"model.state_dict()"
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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": "original-warehouse",
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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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