281 lines
23 KiB
Plaintext
281 lines
23 KiB
Plaintext
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Back_propagation.ipynb",
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"provenance": [],
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"collapsed_sections": [],
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"include_colab_link": true
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},
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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": "3.7.6"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/PacktPublishing/Modern-Computer-Vision-with-PyTorch/blob/master/Chapter01/Back_propagation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-09-24T13:36:49.845714Z",
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"start_time": "2020-09-24T13:36:49.666592Z"
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},
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"id": "TTWK_bQBC7mf"
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},
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"source": [
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"import numpy as np \n",
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"from copy import deepcopy\n",
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"import matplotlib.pyplot as plt\n",
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"x = np.array([[1,1]])\n",
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"y = np.array([[0]])"
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],
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"execution_count": 1,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-09-24T13:36:49.850377Z",
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"start_time": "2020-09-24T13:36:49.846832Z"
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},
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"id": "V8f13jAKC9Rv"
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},
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"source": [
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"from copy import deepcopy\n",
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"import numpy as np\n",
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"def feed_forward(inputs, outputs, weights): \n",
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" pre_hidden = np.dot(inputs,weights[0])+ weights[1]\n",
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" hidden = 1/(1+np.exp(-pre_hidden))\n",
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" out = np.dot(hidden, weights[2]) + weights[3]\n",
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" mean_squared_error = np.mean(np.square(out - outputs))\n",
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|
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" return mean_squared_error"
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],
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"execution_count": 2,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-09-24T13:36:49.855575Z",
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"start_time": "2020-09-24T13:36:49.851797Z"
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},
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"id": "8mF3nQbMC_Ja"
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},
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"source": [
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"def update_weights(inputs, outputs, weights, lr):\n",
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" original_weights = deepcopy(weights)\n",
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" temp_weights = deepcopy(weights)\n",
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" updated_weights = deepcopy(weights)\n",
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" original_loss = feed_forward(inputs, outputs, original_weights)\n",
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" for i, layer in enumerate(original_weights):\n",
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" for index, weight in np.ndenumerate(layer):\n",
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" temp_weights = deepcopy(weights)\n",
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" temp_weights[i][index] += 0.0001\n",
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" _loss_plus = feed_forward(inputs, outputs, temp_weights)\n",
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" grad = (_loss_plus - original_loss)/(0.0001)\n",
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" updated_weights[i][index] -= grad*lr\n",
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" return updated_weights, original_loss"
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],
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-09-24T13:36:49.859883Z",
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"start_time": "2020-09-24T13:36:49.856643Z"
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},
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"id": "jgka7ZyOC_Ow",
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"scrolled": false
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},
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"source": [
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"W = [\n",
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" np.array([[-0.0053, 0.3793],\n",
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" [-0.5820, -0.5204],\n",
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" [-0.2723, 0.1896]], dtype=np.float32).T, \n",
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" np.array([-0.0140, 0.5607, -0.0628], dtype=np.float32), \n",
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" np.array([[ 0.1528, -0.1745, -0.1135]], dtype=np.float32).T, \n",
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" np.array([-0.5516], dtype=np.float32)\n",
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"]"
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],
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"execution_count": 4,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-09-24T13:36:50.261156Z",
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"start_time": "2020-09-24T13:36:49.860952Z"
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},
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"id": "EFr7P1F0C_L_",
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"colab": {
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|
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"base_uri": "https://localhost:8080/",
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"height": 298
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},
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|
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"outputId": "2a439af3-e2a1-4605-acf1-d8d2b874b1e3"
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},
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"source": [
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"losses = []\n",
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|
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"for epoch in range(100):\n",
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" W, loss = update_weights(x,y,W,0.01)\n",
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" losses.append(loss)\n",
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"plt.plot(losses)\n",
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"plt.title('Loss over increasing number of epochs')"
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],
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"Text(0.5, 1.0, 'Loss over increasing number of epochs')"
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]
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},
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"metadata": {
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"tags": []
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|
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},
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|
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"execution_count": 5
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|
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},
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|
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{
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"output_type": "display_data",
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"data": {
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"image/png": "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"text/plain": [
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|
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"<Figure size 432x288 with 1 Axes>"
|
||
|
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]
|
||
|
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},
|
||
|
|
"metadata": {
|
||
|
|
"tags": [],
|
||
|
|
"needs_background": "light"
|
||
|
|
}
|
||
|
|
}
|
||
|
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]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
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"end_time": "2020-09-24T13:36:50.265635Z",
|
||
|
|
"start_time": "2020-09-24T13:36:50.262366Z"
|
||
|
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},
|
||
|
|
"id": "Hyul1G_CDFy_",
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
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},
|
||
|
|
"outputId": "f0ce94c6-9902-4c16-def4-0b6b6f9f436c"
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||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"W"
|
||
|
|
],
|
||
|
|
"execution_count": 6,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
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|
|
"data": {
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|
"text/plain": [
|
||
|
|
"[array([[ 0.01424004, -0.5907864 , -0.27549535],\n",
|
||
|
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" [ 0.39883757, -0.52918637, 0.18640439]], dtype=float32),\n",
|
||
|
|
" array([ 0.00554004, 0.5519136 , -0.06599568], dtype=float32),\n",
|
||
|
|
" array([[ 0.3475135 ],\n",
|
||
|
|
" [-0.05529078],\n",
|
||
|
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" [ 0.03760847]], dtype=float32),\n",
|
||
|
|
" array([-0.22443289], dtype=float32)]"
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||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
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|
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"tags": []
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},
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|
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"execution_count": 6
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-09-24T13:36:50.269686Z",
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|
|
"start_time": "2020-09-24T13:36:50.266775Z"
|
||
|
|
},
|
||
|
|
"id": "zWGav-f7DIhI"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"pre_hidden = np.dot(x,W[0]) + W[1]\n",
|
||
|
|
"hidden = 1/(1+np.exp(-pre_hidden))\n",
|
||
|
|
"out = np.dot(hidden, W[2]) + W[3]"
|
||
|
|
],
|
||
|
|
"execution_count": 7,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-09-24T13:36:50.273559Z",
|
||
|
|
"start_time": "2020-09-24T13:36:50.271055Z"
|
||
|
|
},
|
||
|
|
"id": "30Wqe4KADQFE",
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"outputId": "f81dacdc-4efe-4cdf-ef39-e6ffb19e4ca9"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"out"
|
||
|
|
],
|
||
|
|
"execution_count": 8,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([[-0.0174781]])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"execution_count": 8
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "QqapuMCwDQiJ"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
""
|
||
|
|
],
|
||
|
|
"execution_count": 8,
|
||
|
|
"outputs": []
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|