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Commit abab5ab

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author
codebasics
committed
word embeddings
1 parent 409e7e2 commit abab5ab

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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": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from tensorflow.keras.preprocessing.text import one_hot\n",
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"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.layers import Dense\n",
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"from tensorflow.keras.layers import Flatten\n",
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"from tensorflow.keras.layers import Embedding\n",
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"\n",
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"reviews = ['nice food',\n",
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" 'amazing restaurant',\n",
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" 'too good',\n",
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" 'just loved it!',\n",
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" 'will go again',\n",
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" 'horrible food',\n",
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" 'never go there',\n",
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" 'poor service',\n",
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" 'poor quality',\n",
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" 'needs improvement']\n",
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"\n",
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"sentiment = np.array([1,1,1,1,1,0,0,0,0,0])"
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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": 20,
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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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"[4, 23]"
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]
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},
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"execution_count": 20,
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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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"one_hot(\"amazing restaurant\",30)"
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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": 21,
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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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"[[13, 21], [4, 23], [14, 17], [8, 15, 16], [22, 15, 29], [8, 21], [26, 15, 24], [16, 4], [16, 12], [4, 29]]\n"
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]
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}
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],
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"source": [
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"vocab_size = 30\n",
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"encoded_reviews = [one_hot(d, vocab_size) for d in reviews]\n",
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"print(encoded_reviews)"
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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": 22,
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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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"[[13 21 0 0]\n",
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" [ 4 23 0 0]\n",
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" [14 17 0 0]\n",
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" [ 8 15 16 0]\n",
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" [22 15 29 0]\n",
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" [ 8 21 0 0]\n",
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" [26 15 24 0]\n",
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" [16 4 0 0]\n",
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" [16 12 0 0]\n",
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" [ 4 29 0 0]]\n"
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]
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}
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],
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"source": [
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"max_length = 4\n",
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"padded_reviews = pad_sequences(encoded_reviews, maxlen=max_length, padding='post')\n",
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"print(padded_reviews)"
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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": 23,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"embeded_vector_size = 5\n",
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"\n",
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"model = Sequential()\n",
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"model.add(Embedding(vocab_size, embeded_vector_size, input_length=max_length,name=\"embedding\"))\n",
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"model.add(Flatten())\n",
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"model.add(Dense(1, activation='sigmoid'))"
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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": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = padded_reviews\n",
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"y = sentiment"
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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": 25,
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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: \"sequential_1\"\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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"embedding (Embedding) (None, 4, 5) 150 \n",
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"_________________________________________________________________\n",
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"flatten_1 (Flatten) (None, 20) 0 \n",
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"_________________________________________________________________\n",
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"dense_1 (Dense) (None, 1) 21 \n",
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"=================================================================\n",
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"Total params: 171\n",
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"Trainable params: 171\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n",
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"None\n"
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]
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}
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],
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"source": [
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"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
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"print(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": 26,
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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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"<tensorflow.python.keras.callbacks.History at 0x1bb8daa5a30>"
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]
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},
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"execution_count": 26,
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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, y, epochs=50, verbose=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": 29,
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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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"1/1 [==============================] - 0s 1ms/step - loss: 0.6384 - accuracy: 1.0000\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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"1.0"
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]
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},
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"execution_count": 29,
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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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"# evaluate the model\n",
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"loss, accuracy = model.evaluate(X, y)\n",
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"accuracy"
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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": 30,
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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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"30"
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]
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},
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"execution_count": 30,
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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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"weights = model.get_layer('embedding').get_weights()[0]\n",
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"len(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": 31,
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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([-0.08330977, -0.06752131, -0.04629624, -0.00765801, -0.02024159],\n",
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" dtype=float32)"
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]
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},
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"execution_count": 31,
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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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"weights[13]"
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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": 32,
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"metadata": {
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"scrolled": false
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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([-0.07935128, -0.08574004, 0.06615968, -0.02349528, 0.00917289],\n",
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" dtype=float32)"
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]
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},
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"execution_count": 32,
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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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"weights[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": 33,
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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([ 0.0128377 , 0.03549778, 0.05134471, -0.07147218, 0.03261041],\n",
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" dtype=float32)"
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]
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},
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"execution_count": 33,
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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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"weights[16]"
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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": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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