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‎.gitignore

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.ipynb_checkpoints/

‎卷积.ipynb

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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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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"import numpy as np"
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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": 2,
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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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"'1.9.0'"
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]
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},
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"execution_count": 2,
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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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"tf.__version__"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 核，输入"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"kernel = np.arange(1, 10).reshape((3, 3))"
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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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"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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"array([[1, 2, 3],\n",
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" [4, 5, 6],\n",
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" [7, 8, 9]])"
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]
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},
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"execution_count": 4,
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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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"kernel"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"C = np.array([[1, 2, 3, 0, 4, 5, 6, 0, 7, 8, 9, 0, 0, 0, 0, 0],\n",
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" [0, 1, 2, 3, 0, 4, 5, 6, 0, 7, 8, 9, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 1, 2, 3, 0, 4, 5, 6, 0, 7, 8, 9, 0],\n",
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" [0, 0, 0, 0, 0, 1, 2, 3, 0, 4, 5, 6, 0, 7, 8, 9]])"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"input_ = np.arange(1, 17).reshape((4, 4))"
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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": 7,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 1, 2, 3, 4],\n",
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" [ 5, 6, 7, 8],\n",
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" [ 9, 10, 11, 12],\n",
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" [13, 14, 15, 16]])"
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]
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},
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"execution_count": 7,
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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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"input_"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 卷积 in Numpy"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = input_.reshape((-1, 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": 9,
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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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"array([[ 1],\n",
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" [ 2],\n",
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" [ 3],\n",
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" [ 4],\n",
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" [ 5],\n",
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" [ 6],\n",
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" [ 7],\n",
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" [ 8],\n",
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" [ 9],\n",
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" [10],\n",
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" [11],\n",
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" [12],\n",
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" [13],\n",
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" [14],\n",
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" [15],\n",
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" [16]])"
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]
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},
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"execution_count": 9,
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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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"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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"y = C.dot(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": 11,
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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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"array([[348],\n",
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" [393],\n",
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" [528],\n",
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" [573]])"
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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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"y"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"y = y.reshape((2, 2))"
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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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"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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"array([[348, 393],\n",
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" [528, 573]])"
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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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"y"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 卷积 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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = tf.expand_dims(tf.expand_dims(tf.constant(input_, dtype=tf.float32), axis=-1), axis=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": 15,
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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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"[1, 4, 4, 1]"
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]
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},
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"execution_count": 15,
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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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"x.get_shape().as_list()"
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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": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"kernel_conv = tf.expand_dims(tf.expand_dims(tf.constant(kernel, dtype=tf.float32), axis=-1), axis=-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": 17,
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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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"[3, 3, 1, 1]"
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]
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},
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"execution_count": 17,
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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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"kernel_conv.get_shape().as_list()"
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"y = tf.nn.conv2d(x, kernel_conv, strides=(1, 1, 1, 1), padding='VALID')"
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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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"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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"[[348. 393.]\n",
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" [528. 573.]]\n"
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]
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}
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],
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"source": [
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"with tf.Session() as sess:\n",
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" print(y.eval()[0, :, :, 0])"
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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",
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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": "3.6.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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