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102 lines
1.9 KiB
102 lines
1.9 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eefe7571",
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"metadata": {},
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"outputs": [],
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"source": [
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"# show differentiation in Tensorflow"
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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": "a9d7c185",
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf"
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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": "584384f1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define a function to differentiate\n",
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"def f(x):\n",
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" return x ** 2 + 2 * x + 1"
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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": "70430402",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a TensorFlow variable\n",
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"x = tf.Variable(2.0)"
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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": "45ea0a33",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Use tf.GradientTape to record the gradients\n",
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"with tf.GradientTape() as tape:\n",
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" y = f(x)"
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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": "f6b1ff27",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calculate the gradient of y with respect to x\n",
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"dy_dx = tape.gradient(y, x)"
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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": "4f581817",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Print the result\n",
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"print(dy_dx)"
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]
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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 (ipykernel)",
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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": "3.8.16"
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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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