my version
This commit is contained in:
oskar
parent
86a62efd36
commit
72d8610743
322
work-sc.ipynb
322
work-sc.ipynb
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"id": "initial_id",
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"metadata": {
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"end_time": "2025-11-04T21:43:32.795850Z",
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}
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},
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"source": "import numpy as np",
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"outputs": [],
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"execution_count": 45
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"execution_count": 84
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{
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"id": "48cafaf4b64967bb",
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"outputs": [],
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"id": "d13137630b41b756",
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"outputs": [],
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],
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"id": "31f205147667dea6",
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"execution_count": 87
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"cell_type": "code",
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@ -106,13 +106,13 @@
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],
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"id": "c1b960e7dcf09d91",
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"outputs": [],
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"execution_count": 88
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{
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"metadata": {
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@ -131,13 +131,13 @@
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],
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"id": "efae2e184daf2fce",
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"outputs": [],
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"execution_count": 61
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"execution_count": 89
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},
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{
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"end_time": "2025-11-04T21:43:33.055558Z",
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"cell_type": "code",
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@ -162,13 +162,13 @@
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],
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"id": "c3cd9e8f51dbe967",
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"outputs": [],
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"execution_count": 51
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"execution_count": 90
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{
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"metadata": {
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"cell_type": "code",
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@ -191,13 +191,13 @@
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],
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"id": "121416e7bbab57bb",
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"outputs": [],
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"execution_count": 52
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"execution_count": 91
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:43:33.176375Z",
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}
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},
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"cell_type": "code",
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@ -205,9 +205,9 @@
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"def single_layer_backward_propagation(dA_curr, W_curr, b_curr, Z_curr, A_prev, activation=\"relu\"):\n",
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" m = A_prev.shape[1]\n",
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"\n",
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" if activation is \"relu\":\n",
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" if activation == \"relu\":\n",
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" backward_activation_func = relu_backward\n",
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" elif activation is \"sigmoid\":\n",
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" elif activation == \"sigmoid\":\n",
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" backward_activation_func = sigmoid_backward\n",
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" else:\n",
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" raise Exception('Non-supported activation function')\n",
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@ -221,13 +221,13 @@
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],
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"id": "92e4b87664f18a63",
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"outputs": [],
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"execution_count": 53
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"execution_count": 92
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:43:33.243823Z",
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"start_time": "2025-11-04T21:43:33.234283Z"
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"end_time": "2025-11-04T22:00:01.259385Z",
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"start_time": "2025-11-04T22:00:01.253050Z"
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}
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"cell_type": "code",
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@ -260,13 +260,13 @@
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],
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"id": "2c8e4eed1846f003",
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"outputs": [],
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"execution_count": 54
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"execution_count": 93
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},
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{
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"metadata": {
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"cell_type": "code",
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@ -281,13 +281,13 @@
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],
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"id": "16320b953a183511",
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"outputs": [],
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"execution_count": 66
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"execution_count": 94
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"id": "fce33f70bba3898",
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"outputs": [],
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"execution_count": 95
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@ -359,13 +359,13 @@
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],
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"id": "cccd73b5018799d4",
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"outputs": [],
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"execution_count": 96
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],
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"execution_count": 97
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@ -393,13 +393,13 @@
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@ -409,13 +409,13 @@
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"id": "ce04892d496c5147",
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"outputs": [],
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"execution_count": 77
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"execution_count": 99
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"end_time": "2025-11-04T22:00:11.428146Z",
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"cell_type": "code",
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@ -435,83 +435,13 @@
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]
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"execution_count": 78
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"execution_count": 105
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"cell_type": "code",
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"source": [
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"startA = np.random.randn(nn_architecture[0][\"input_dim\"],1) * 0.1\n",
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"full_forward_propagation(startA, params, nn_architecture)"
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],
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"id": "8b672c5fd5832cc",
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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([[0.51608074]]),\n",
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" {'A0': array([[-0.10166672],\n",
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" [ 0.14706683]]),\n",
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" 'Z1': array([[ 0.0203953 ],\n",
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" [-0.22010647],\n",
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" [-0.01614817],\n",
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" [ 0.07300465]]),\n",
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" 'Z2': array([[-0.18085747],\n",
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" [-0.01827604],\n",
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" [-0.21683156],\n",
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" [ 0.08504111],\n",
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" [ 0.17066065],\n",
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" [0. ]]),\n",
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" 'Z3': array([[-0.17707529],\n",
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" [ 0.0237745 ],\n",
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" [-0.07487052],\n",
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" [-0.02497606],\n",
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" [ 0.12622027],\n",
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" [ 0.02613133]]),\n",
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" 'A3': array([[0. ],\n",
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" [0.0237745 ],\n",
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" [0. ],\n",
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" [0. ],\n",
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" [0.12622027],\n",
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" [0.02613133]]),\n",
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" 'Z4': array([[-0.09066425],\n",
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" [ 0.05792425],\n",
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" [ 0.07822296],\n",
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" [ 0.07317913]]),\n",
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" 'A4': array([[0. ],\n",
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" [0.05792425],\n",
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" [0.07822296],\n",
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" [0.07317913]]),\n",
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" 'Z5': array([[0.06434517]])})"
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]
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},
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"execution_count": 24,
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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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"execution_count": 24
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:52:07.296371Z",
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"start_time": "2025-11-04T21:52:01.384867Z"
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"end_time": "2025-11-04T22:00:29.176357Z",
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"start_time": "2025-11-04T22:00:23.282276Z"
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}
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},
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"cell_type": "code",
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@ -535,47 +465,157 @@
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"output_type": "stream",
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"text": [
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"/home/oskar/projects/nn-from-scratch/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:95: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
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" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
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"2025-11-04 22:52:01.409083: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW\n",
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"2025-11-04 22:52:01.409097: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:171] verbose logging is disabled. Rerun with verbose logging (usually --v=1 or --vmodule=cuda_diagnostics=1) to get more diagnostic output from this module\n",
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"2025-11-04 22:52:01.409099: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:176] retrieving CUDA diagnostic information for host: solaria\n",
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"2025-11-04 22:52:01.409101: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:183] hostname: solaria\n",
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"2025-11-04 22:52:01.409176: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] libcuda reported version is: 580.95.5\n",
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"2025-11-04 22:52:01.409184: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:194] kernel reported version is: 570.195.3\n",
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"2025-11-04 22:52:01.409185: E external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:287] kernel version 570.195.3 does not match DSO version 580.95.5 -- cannot find working devices in this configuration\n"
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" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
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]
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}
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],
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"execution_count": 79
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"execution_count": 106
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-04T21:53:11.479872Z",
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"start_time": "2025-11-04T21:53:11.455625Z"
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"end_time": "2025-11-04T22:00:33.380478Z",
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"start_time": "2025-11-04T22:00:33.309269Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"Y_test_hat = model.predict_classes(X_test)\n",
|
||||
"Y_test_prob = model.predict(X_test)\n",
|
||||
"Y_test_hat = (Y_test_prob > 0.5).astype(int).ravel()\n",
|
||||
"acc_test = accuracy_score(y_test, Y_test_hat)\n",
|
||||
"print(\"Test set accuracy: {:.2f} - Goliath\".format(acc_test))"
|
||||
],
|
||||
"id": "ef52bee9c93081d3",
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:tensorflow:6 out of the last 10 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7e21f476c900> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
|
||||
"\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step \n",
|
||||
"Test set accuracy: 0.99 - Goliath\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": 107
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-11-04T22:03:33.972219Z",
|
||||
"start_time": "2025-11-04T22:03:33.966407Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"def make_plot(X, y, plot_name, file_name=None, XX=None, YY=None, preds=None, dark=False):\n",
|
||||
" if (dark):\n",
|
||||
" plt.style.use('dark_background')\n",
|
||||
" else:\n",
|
||||
" sns.set_style(\"whitegrid\")\n",
|
||||
" plt.figure(figsize=(16,12))\n",
|
||||
" axes = plt.gca()\n",
|
||||
" axes.set(xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
|
||||
" plt.title(plot_name, fontsize=30)\n",
|
||||
" plt.subplots_adjust(left=0.20)\n",
|
||||
" plt.subplots_adjust(right=0.80)\n",
|
||||
" if(XX is not None and YY is not None and preds is not None):\n",
|
||||
" plt.contourf(XX, YY, preds.reshape(XX.shape), 25, alpha = 1, cmap=cm.Spectral)\n",
|
||||
" plt.contour(XX, YY, preds.reshape(XX.shape), levels=[.5], cmap=\"Greys\", vmin=0, vmax=.6)\n",
|
||||
" plt.scatter(X[:, 0], X[:, 1], c=y.ravel(), s=40, cmap=plt.cm.Spectral, edgecolors='black')\n",
|
||||
" if(file_name):\n",
|
||||
" plt.savefig(file_name)\n",
|
||||
" plt.close()"
|
||||
],
|
||||
"id": "9535365d1da72395",
|
||||
"outputs": [],
|
||||
"execution_count": 109
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-11-04T22:02:51.938430Z",
|
||||
"start_time": "2025-11-04T22:02:51.934316Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"# boundary of the graph\n",
|
||||
"GRID_X_START = -1.5\n",
|
||||
"GRID_X_END = 2.5\n",
|
||||
"GRID_Y_START = -1.0\n",
|
||||
"GRID_Y_END = 2\n",
|
||||
"# output directory (the folder must be created on the drive)\n",
|
||||
"OUTPUT_DIR = \"./binary_classification_vizualizations/\"\n",
|
||||
"### Definition of grid boundaries\n",
|
||||
"grid = np.mgrid[GRID_X_START:GRID_X_END:100j, GRID_X_START:GRID_Y_END:100j]\n",
|
||||
"grid_2d = grid.reshape(2, -1).T\n",
|
||||
"XX, YY = grid"
|
||||
],
|
||||
"id": "b070f03d55981894",
|
||||
"outputs": [],
|
||||
"execution_count": 108
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-11-04T22:05:02.290039Z",
|
||||
"start_time": "2025-11-04T22:05:02.042691Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"def callback_keras_plot(epoch, logs):\n",
|
||||
" plot_title = \"Keras Model - It: {:05}\".format(epoch)\n",
|
||||
" file_name = \"keras_model_{:05}.png\".format(epoch)\n",
|
||||
" file_path = os.path.join(OUTPUT_DIR, file_name)\n",
|
||||
" prediction_probs = model.predict_proba(grid_2d, batch_size=32, verbose=0)\n",
|
||||
" make_plot(X_test, y_test, plot_title, file_name=file_path, XX=XX, YY=YY, preds=prediction_probs)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Adding callback functions that they will run in every epoch\n",
|
||||
"testmodelcb = keras.callbacks.LambdaCallback(on_epoch_end=callback_keras_plot)\n",
|
||||
"\n",
|
||||
"# Building a model\n",
|
||||
"model = Sequential()\n",
|
||||
"model.add(Dense(25, input_dim=2, activation='relu'))\n",
|
||||
"model.add(Dense(50, activation='relu'))\n",
|
||||
"model.add(Dense(50, activation='relu'))\n",
|
||||
"model.add(Dense(25, activation='relu'))\n",
|
||||
"model.add(Dense(1, activation='sigmoid'))\n",
|
||||
"\n",
|
||||
"model.compile(loss='binary_crossentropy', optimizer=\"sgd\", metrics=['accuracy'])\n",
|
||||
"\n",
|
||||
"# Training\n",
|
||||
"history = model.fit(X_train, y_train, epochs=200, verbose=0, callbacks=[testmodelcb])\n",
|
||||
"rediction_probs = model.predict_proba(grid_2d, batch_size=32, verbose=0)\n",
|
||||
"make_plot(X_test, y_test, \"Keras Model\", file_name=None, XX=XX, YY=YY, preds=prediction_probs)"
|
||||
],
|
||||
"id": "6feab7da06e7a828",
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/oskar/projects/nn-from-scratch/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:95: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
|
||||
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "AttributeError",
|
||||
"evalue": "'Sequential' object has no attribute 'predict_classes'",
|
||||
"evalue": "'Sequential' object has no attribute 'predict_proba'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
|
||||
"\u001B[31mAttributeError\u001B[39m Traceback (most recent call last)",
|
||||
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[83]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m Y_test_hat = \u001B[43mmodel\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpredict_classes\u001B[49m(X_test)\n\u001B[32m 2\u001B[39m acc_test = accuracy_score(y_test, Y_test_hat)\n\u001B[32m 3\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[33mTest set accuracy: \u001B[39m\u001B[38;5;132;01m{:.2f}\u001B[39;00m\u001B[33m - Goliath\u001B[39m\u001B[33m\"\u001B[39m.format(acc_test))\n",
|
||||
"\u001B[31mAttributeError\u001B[39m: 'Sequential' object has no attribute 'predict_classes'"
|
||||
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[110]\u001B[39m\u001B[32m, line 23\u001B[39m\n\u001B[32m 20\u001B[39m model.compile(loss=\u001B[33m'\u001B[39m\u001B[33mbinary_crossentropy\u001B[39m\u001B[33m'\u001B[39m, optimizer=\u001B[33m\"\u001B[39m\u001B[33msgd\u001B[39m\u001B[33m\"\u001B[39m, metrics=[\u001B[33m'\u001B[39m\u001B[33maccuracy\u001B[39m\u001B[33m'\u001B[39m])\n\u001B[32m 22\u001B[39m \u001B[38;5;66;03m# Training\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m23\u001B[39m history = \u001B[43mmodel\u001B[49m\u001B[43m.\u001B[49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m200\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mverbose\u001B[49m\u001B[43m=\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[43m=\u001B[49m\u001B[43m[\u001B[49m\u001B[43mtestmodelcb\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 24\u001B[39m rediction_probs = model.predict_proba(grid_2d, batch_size=\u001B[32m32\u001B[39m, verbose=\u001B[32m0\u001B[39m)\n\u001B[32m 25\u001B[39m make_plot(X_test, y_test, \u001B[33m\"\u001B[39m\u001B[33mKeras Model\u001B[39m\u001B[33m\"\u001B[39m, file_name=\u001B[38;5;28;01mNone\u001B[39;00m, XX=XX, YY=YY, preds=prediction_probs)\n",
|
||||
"\u001B[36mFile \u001B[39m\u001B[32m~/projects/nn-from-scratch/.venv/lib/python3.13/site-packages/keras/src/utils/traceback_utils.py:122\u001B[39m, in \u001B[36mfilter_traceback.<locals>.error_handler\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 119\u001B[39m filtered_tb = _process_traceback_frames(e.__traceback__)\n\u001B[32m 120\u001B[39m \u001B[38;5;66;03m# To get the full stack trace, call:\u001B[39;00m\n\u001B[32m 121\u001B[39m \u001B[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m122\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m e.with_traceback(filtered_tb) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m 123\u001B[39m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[32m 124\u001B[39m \u001B[38;5;28;01mdel\u001B[39;00m filtered_tb\n",
|
||||
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[110]\u001B[39m\u001B[32m, line 5\u001B[39m, in \u001B[36mcallback_keras_plot\u001B[39m\u001B[34m(epoch, logs)\u001B[39m\n\u001B[32m 3\u001B[39m file_name = \u001B[33m\"\u001B[39m\u001B[33mkeras_model_\u001B[39m\u001B[38;5;132;01m{:05}\u001B[39;00m\u001B[33m.png\u001B[39m\u001B[33m\"\u001B[39m.format(epoch)\n\u001B[32m 4\u001B[39m file_path = os.path.join(OUTPUT_DIR, file_name)\n\u001B[32m----> \u001B[39m\u001B[32m5\u001B[39m prediction_probs = \u001B[43mmodel\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpredict_proba\u001B[49m(grid_2d, batch_size=\u001B[32m32\u001B[39m, verbose=\u001B[32m0\u001B[39m)\n\u001B[32m 6\u001B[39m make_plot(X_test, y_test, plot_title, file_name=file_path, XX=XX, YY=YY, preds=prediction_probs)\n",
|
||||
"\u001B[31mAttributeError\u001B[39m: 'Sequential' object has no attribute 'predict_proba'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": 83
|
||||
"execution_count": 110
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
Loading…
Reference in a new issue