mirror of
https://github.com/socathie/circomlib-ml.git
synced 2026-01-10 06:28:08 -05:00
397 lines
16 KiB
Plaintext
397 lines
16 KiB
Plaintext
{
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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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"from tensorflow.keras.layers import Input, Conv2D, Dense, ReLU, Flatten, Softmax\n",
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"from tensorflow.keras import Model\n",
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"from tensorflow.keras.datasets import mnist\n",
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"from tensorflow.keras.utils import to_categorical\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
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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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"source": [
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"(X_train, y_train), (X_test, y_test) = mnist.load_data()"
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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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"# Convert y_train into one-hot format\n",
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"temp = []\n",
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"for i in range(len(y_train)):\n",
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" temp.append(to_categorical(y_train[i], num_classes=10))\n",
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"y_train = np.array(temp)\n",
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"# Convert y_test into one-hot format\n",
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"temp = []\n",
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"for i in range(len(y_test)): \n",
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" temp.append(to_categorical(y_test[i], num_classes=10))\n",
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"y_test = np.array(temp)"
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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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"source": [
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"#reshaping\n",
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"X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)\n",
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"X_test = X_test.reshape(X_test.shape[0], 28, 28, 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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"inputs = Input(shape=(28,28,1))\n",
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"out = Conv2D(1,3)(inputs)\n",
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"out = ReLU()(out)\n",
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"out = Flatten()(out)\n",
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"out = Dense(10, activation=None)(out)\n",
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"out = Softmax()(out)\n",
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"model = Model(inputs, out)"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model: \"model\"\n",
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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"input_1 (InputLayer) [(None, 28, 28, 1)] 0 \n",
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"_________________________________________________________________\n",
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"conv2d (Conv2D) (None, 26, 26, 1) 10 \n",
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"_________________________________________________________________\n",
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"re_lu (ReLU) (None, 26, 26, 1) 0 \n",
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"_________________________________________________________________\n",
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"flatten (Flatten) (None, 676) 0 \n",
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"_________________________________________________________________\n",
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"dense (Dense) (None, 10) 6770 \n",
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"_________________________________________________________________\n",
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"softmax (Softmax) (None, 10) 0 \n",
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"=================================================================\n",
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"Total params: 6,780\n",
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"Trainable params: 6,780\n",
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"Non-trainable params: 0\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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"model.summary()"
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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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"outputs": [],
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"source": [
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"model.compile(\n",
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" loss='categorical_crossentropy',\n",
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" optimizer='adam',\n",
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" metrics=['acc']\n",
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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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"execution_count": 8,
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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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"Epoch 1/10\n",
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"1875/1875 [==============================] - 1s 640us/step - loss: 19.4036 - acc: 0.7586 - val_loss: 1.0043 - val_acc: 0.8697\n",
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"Epoch 2/10\n",
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"1875/1875 [==============================] - 1s 483us/step - loss: 0.6379 - acc: 0.8767 - val_loss: 0.3041 - val_acc: 0.9168\n",
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"Epoch 3/10\n",
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"1875/1875 [==============================] - 1s 461us/step - loss: 0.3112 - acc: 0.9124 - val_loss: 0.2960 - val_acc: 0.9166\n",
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"Epoch 4/10\n",
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"1875/1875 [==============================] - 1s 486us/step - loss: 0.3033 - acc: 0.9151 - val_loss: 0.2906 - val_acc: 0.9196\n",
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"Epoch 5/10\n",
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"1875/1875 [==============================] - 1s 462us/step - loss: 0.3027 - acc: 0.9152 - val_loss: 0.2918 - val_acc: 0.9184\n",
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"Epoch 6/10\n",
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"1875/1875 [==============================] - 1s 499us/step - loss: 0.3119 - acc: 0.9163 - val_loss: 0.2949 - val_acc: 0.9206\n",
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"Epoch 7/10\n",
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"1875/1875 [==============================] - 1s 459us/step - loss: 0.3006 - acc: 0.9178 - val_loss: 0.2878 - val_acc: 0.9163\n",
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"Epoch 8/10\n",
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"1875/1875 [==============================] - 1s 481us/step - loss: 0.2898 - acc: 0.9219 - val_loss: 0.2750 - val_acc: 0.9246\n",
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"Epoch 9/10\n",
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"1875/1875 [==============================] - 1s 457us/step - loss: 0.2803 - acc: 0.9232 - val_loss: 0.2823 - val_acc: 0.9243\n",
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"Epoch 10/10\n",
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"1875/1875 [==============================] - 1s 483us/step - loss: 0.2751 - acc: 0.9244 - val_loss: 0.3078 - val_acc: 0.9240\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<tensorflow.python.keras.callbacks.History at 0x156182c70>"
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]
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},
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"execution_count": 8,
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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.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))"
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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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"(28, 28, 1)"
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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 = X_test[0]\n",
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"X.shape"
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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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"model2 = Model(model.input, model.layers[-2].output)"
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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([[ -4.7280216, -14.685291 , -1.7771893, 4.1886683, -5.325138 ,\n",
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" -2.5930152, -11.220819 , 8.523948 , -1.0928547, 1.6447238]],\n",
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" dtype=float32)"
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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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"model2.predict(X_test[[0]]) - model.weights[3].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": 12,
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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([[9.0031011e-07, 1.0410367e-10, 2.1521399e-05, 5.5709118e-03,\n",
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" 8.1659567e-07, 1.7811672e-05, 1.4445434e-09, 9.9381268e-01,\n",
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" 1.9175804e-05, 5.5624702e-04]], dtype=float32)"
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]
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},
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"execution_count": 12,
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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.predict(X_test[[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": 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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"<matplotlib.image.AxesImage at 0x157410be0>"
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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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"data": {
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"image/png": "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",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
|
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
|
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"source": [
|
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"plt.imshow(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": 14,
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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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"[<tf.Variable 'conv2d/kernel:0' shape=(3, 3, 1, 1) dtype=float32, numpy=\n",
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" array([[[[-0.0031894 ]],\n",
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" \n",
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" [[ 0.01395892]],\n",
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" \n",
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" [[-0.01202957]]],\n",
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" \n",
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" \n",
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" [[[ 0.01273286]],\n",
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" \n",
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" [[ 0.00727907]],\n",
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" \n",
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" [[-0.00096878]]],\n",
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" \n",
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" \n",
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" [[[-0.01515301]],\n",
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" \n",
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" [[ 0.00046251]],\n",
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" \n",
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" [[ 0.00114259]]]], dtype=float32)>,\n",
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" <tf.Variable 'conv2d/bias:0' shape=(1,) dtype=float32, numpy=array([0.01268097], dtype=float32)>,\n",
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" <tf.Variable 'dense/kernel:0' shape=(676, 10) dtype=float32, numpy=\n",
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" array([[-0.20955266, 0.40033442, -0.1978653 , ..., 0.32269812,\n",
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" -0.2694615 , 0.06487904],\n",
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" [-0.07851421, 0.36309943, -0.11626323, ..., 0.15947255,\n",
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" -0.3209268 , 0.13795587],\n",
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" [-0.21611924, 0.3673279 , -0.12897709, ..., 0.32853407,\n",
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" -0.4003139 , 0.03121791],\n",
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" ...,\n",
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" [-0.22975925, 0.25338897, -0.34073418, ..., -0.11573094,\n",
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" -0.19876844, 0.06433479],\n",
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" [-0.23668756, 0.3116613 , -0.12199575, ..., 0.19775279,\n",
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" -0.16193576, -0.01519678],\n",
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" [-0.22618775, 0.45856324, 0.08593661, ..., 0.2934398 ,\n",
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" -0.3539578 , -0.04992562]], dtype=float32)>,\n",
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" <tf.Variable 'dense/bias:0' shape=(10,) dtype=float32, numpy=\n",
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" array([-0.2101967 , 0.681965 , 0.01303466, -0.39655647, 0.28932407,\n",
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" 0.6396667 , -0.15234365, 0.45215404, -0.7866978 , -0.15671334],\n",
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" dtype=float32)>]"
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]
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},
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"execution_count": 14,
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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.weights"
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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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"source": [
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"in_json = {\n",
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" \"in\": X.astype(int).flatten().tolist(),\n",
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" \"conv2d_weights\": (model.weights[0].numpy()*(10**9)).round().astype(int).flatten().tolist(),\n",
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" \"conv2d_bias\": (model.weights[1].numpy()*(10**9)).round().astype(int).flatten().tolist(), # no need to sqaure the scaling because input was not scaled\n",
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" \"dense_weights\":(model.weights[2].numpy()*(10**9)).round().astype(int).flatten().tolist(),\n",
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" \"dense_bias\": np.zeros(model.weights[3].numpy().shape).tolist() # zero because we are not doing softmax in circom, just argmax\n",
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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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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json"
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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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"source": [
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"with open(\"mnist_input.json\", \"w\") as f:\n",
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" json.dump(in_json, f)"
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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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"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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"interpreter": {
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"hash": "11280bdb37aa6bc5d4cf1e4de756386eb1f9eecd8dcdefa77636dfac7be2370d"
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},
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"kernelspec": {
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"display_name": "Python 3.8.6 ('tf24')",
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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": "3.8.6"
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},
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"orig_nbformat": 4
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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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