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ehzawad/dense.ipynb

Created May 24, 2022 05:10
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Dense layer man. It's a chain of matrix multiplication!
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dense.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 41,
"id": "df620e5c-e3c1-4e62-8cd6-d82da912beb6",
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras import layers\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "69c81347-1dca-47ec-8a56-f7a5d4d49791",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'keras.api._v2.keras.layers' from '/home/ehz/ehz_codespace/lib/python3.10/site-packages/keras/api/_v2/keras/layers/__init__.py'>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"layers"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "09c9a584-55eb-41ca-a71a-36033772f83a",
"metadata": {},
"outputs": [],
"source": [
"layer1 = layers.Dense(32, activation='relu')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "9e9c0046-c70a-4315-b62d-f3ae9bfbba16",
"metadata": {},
"outputs": [],
"source": [
"layer2 = layers.Dense(12, activation='relu')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "cb5284fb-bba8-4a41-af8c-7b7c807b5e7b",
"metadata": {},
"outputs": [],
"source": [
"inputs = tf.random.uniform(shape=(5, 7))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "f1a37254-b87d-4283-a3aa-4174e6155fa7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TensorShape([5, 7])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inputs.shape"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "ec90d756-ec55-488b-906d-02fd6773f727",
"metadata": {},
"outputs": [],
"source": [
"temp = layer1(inputs)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "543193a1-42a2-4b66-9908-0cea57a6db5f",
"metadata": {},
"outputs": [],
"source": [
"output = layer2(temp)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "bab607c8-ec41-4052-b19d-8ec338e68104",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TensorShape([5, 12])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output.shape"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "ed68b308-e73a-4d32-80b0-ab8398a941d4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<tf.Variable 'dense_6/kernel:0' shape=(7, 32) dtype=float32, numpy=\n",
" array([[ 0.05386424, 0.13914755, -0.23303838, 0.38529482, -0.16446574,\n",
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" 0.29787508, -0.04547703, -0.21177053, 0.0519672 , -0.09964156,\n",
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" 0.21059158, -0.2811948 , -0.08648655, -0.34015557, 0.34692863,\n",
" -0.09924281, -0.35676035, -0.3475111 , -0.26425683, 0.07749954,\n",
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" 0.04354903, -0.20802186, -0.37038013, 0.24529973, 0.35060135,\n",
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" -0.3692777 , 0.38994667, 0.26879057, -0.18917952, -0.31989357,\n",
" -0.38851747, -0.0256398 , -0.20003976, -0.1978874 , -0.01654428,\n",
" -0.06172019, -0.38118875],\n",
" [-0.3336207 , 0.05044457, 0.03144121, -0.27515194, -0.1052897 ,\n",
" 0.19087252, -0.07786706, -0.3909658 , -0.16610403, -0.10372519,\n",
" -0.07434395, -0.06366298, 0.05362636, -0.16987345, -0.23021075,\n",
" 0.19312325, 0.37247106, -0.1030041 , 0.02222911, -0.20657527,\n",
" -0.15526222, 0.14027116, 0.2655551 , 0.04057357, 0.18453637,\n",
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" -0.21290393, 0.05877951, 0.03840324, 0.34067717, 0.37284896,\n",
" -0.04059169, -0.03194469, 0.13479325, 0.33770844, 0.20873538,\n",
" 0.3834305 , 0.17489466, 0.29314056, 0.04251027, -0.03381941,\n",
" -0.10580611, 0.273784 , 0.25971124, 0.3618442 , 0.07291859,\n",
" -0.36191708, -0.34782413],\n",
" [ 0.33210185, -0.3738128 , 0.28979656, -0.03456977, 0.30138257,\n",
" 0.20347878, 0.11072072, 0.35756972, 0.02163666, 0.2967598 ,\n",
" 0.28923652, 0.392181 , -0.36035556, 0.27499023, 0.18463507,\n",
" 0.39069787, -0.23184699, -0.3198552 , -0.1811965 , -0.15944517,\n",
" -0.09043121, 0.17203704, -0.35189605, -0.08133459, 0.31767276,\n",
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" -0.00110152, 0.20652947]], dtype=float32)>,\n",
" <tf.Variable 'dense_6/bias:0' shape=(32,) dtype=float32, numpy=\n",
" array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" dtype=float32)>]"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"layer1.weights"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "ec2cf1d9-f2b8-46a0-8b81-eebe65aafead",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<tf.Variable 'dense_7/kernel:0' shape=(32, 12) dtype=float32, numpy=\n",
" array([[ 0.00567561, -0.21147865, 0.3240967 , 0.09782934, 0.31225058,\n",
" -0.07823095, 0.12157503, 0.00756183, -0.25792673, -0.12439474,\n",
" -0.10828915, 0.28356698],\n",
" [-0.21062236, -0.04337543, -0.02095643, -0.01321763, 0.2789137 ,\n",
" 0.10817462, 0.06546751, -0.11163518, 0.30902985, -0.02685955,\n",
" 0.14782682, -0.19354083],\n",
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" 0.25880793, -0.14903758],\n",
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" [-0.2513897 , 0.01480126, -0.31038854, 0.2315751 , -0.12091595,\n",
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" 0.15622637, -0.24193014],\n",
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" -0.21830463, -0.0110203 , -0.02637285, -0.18804286, -0.11756462,\n",
" -0.11586982, -0.2588923 ],\n",
" [-0.2560247 , -0.09329721, 0.3346124 , 0.03635383, 0.18489751,\n",
" 0.11543605, 0.26905623, -0.02172047, -0.02137208, 0.32781097,\n",
" -0.07020786, 0.13078031],\n",
" [ 0.17592093, 0.14790931, 0.31597766, 0.25468054, -0.1061691 ,\n",
" -0.3614721 , -0.25750563, -0.21206915, -0.0954634 , 0.03374586,\n",
" -0.14969094, 0.22436681]], dtype=float32)>,\n",
" <tf.Variable 'dense_7/bias:0' shape=(12,) dtype=float32, numpy=array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)>]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"layer2.weights"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2e8025d-78cc-4159-900a-47c2d336358c",
"metadata": {},
"outputs": [],
"source": [
"class SimpleDense(Layer):\n",
" def __init__(self, units=32)\n",
" super(SimpleDense, self).__init__()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
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}
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