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Sasza Stanczew 2025-05-20 13:31:05 +02:00
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PythonAI/JupyterLab/PythonAI_5.ipynb
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@ -70,9 +70,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "9d98ba73-f954-4a6a-ac09-3a6b1afa58ce", "id": "9d98ba73-f954-4a6a-ac09-3a6b1afa58ce",
"metadata": { "metadata": {},
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},
"source": [ "source": [
"# Kod" "# Kod"
] ]
@ -80,16 +78,14 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "11ee196e-06a8-47a4-a411-aa7dd06faaf8", "id": "11ee196e-06a8-47a4-a411-aa7dd06faaf8",
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"source": [ "source": [
"## Setup i wczytanie danych" "## Setup i wczytanie danych"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 134, "execution_count": 33,
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"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -101,7 +97,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 135, "execution_count": 34,
"id": "4b4881e2-fdaf-4ccc-b68e-15ec7391146e", "id": "4b4881e2-fdaf-4ccc-b68e-15ec7391146e",
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@ -113,7 +109,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 136, "execution_count": 35,
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@ -129,7 +125,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 137, "execution_count": 36,
"id": "beee8e5a-348e-45d1-9a40-79976aed712f", "id": "beee8e5a-348e-45d1-9a40-79976aed712f",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -169,9 +165,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
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"metadata": { "metadata": {},
"jp-MarkdownHeadingCollapsed": true
},
"source": [ "source": [
"## Model" "## Model"
] ]
@ -186,18 +180,26 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 92, "execution_count": 37,
"id": "4ec7f823-09ee-4b22-a2f6-f3f74853211e", "id": "4ec7f823-09ee-4b22-a2f6-f3f74853211e",
"metadata": {}, "metadata": {},
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{
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"/home/sasza/.local/share/virtualenvs/JupyterLab-9JRWupKp/lib/python3.12/site-packages/keras/src/layers/core/dense.py:93: 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"
]
},
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"data": { "data": {
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_15\"</span>\n", "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
"</pre>\n" "</pre>\n"
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"\u001b[1mModel: \"sequential_15\"\u001b[0m\n" "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
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"metadata": {}, "metadata": {},
@ -318,7 +320,7 @@
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"execution_count": 93, "execution_count": 38,
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@ -339,7 +341,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
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@ -347,65 +349,78 @@
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"text": [ "text": [
"Epoch 1/25\n", "Epoch 1/25\n"
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 8ms/step - accuracy: 0.7797 - loss: 0.6840 - val_accuracy: 0.9529 - val_loss: 0.1505\n", ]
},
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"text": [
"2025-05-20 13:23:23.834163: W external/local_xla/xla/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 301056000 exceeds 10% of free system memory.\n"
]
},
{
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"text": [
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 9ms/step - accuracy: 0.7850 - loss: 0.6727 - val_accuracy: 0.9557 - val_loss: 0.1503\n",
"Epoch 2/25\n", "Epoch 2/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9466 - loss: 0.1820 - val_accuracy: 0.9659 - val_loss: 0.1156\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.9457 - loss: 0.1848 - val_accuracy: 0.9625 - val_loss: 0.1182\n",
"Epoch 3/25\n", "Epoch 3/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9579 - loss: 0.1361 - val_accuracy: 0.9683 - val_loss: 0.1057\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.9584 - loss: 0.1359 - val_accuracy: 0.9688 - val_loss: 0.1069\n",
"Epoch 4/25\n", "Epoch 4/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9674 - loss: 0.1131 - val_accuracy: 0.9681 - val_loss: 0.1111\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9668 - loss: 0.1052 - val_accuracy: 0.9682 - val_loss: 0.1080\n",
"Epoch 5/25\n", "Epoch 5/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9706 - loss: 0.0968 - val_accuracy: 0.9723 - val_loss: 0.0927\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9735 - loss: 0.0867 - val_accuracy: 0.9723 - val_loss: 0.0939\n",
"Epoch 6/25\n", "Epoch 6/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9751 - loss: 0.0820 - val_accuracy: 0.9718 - val_loss: 0.0968\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9725 - loss: 0.0900 - val_accuracy: 0.9736 - val_loss: 0.0926\n",
"Epoch 7/25\n", "Epoch 7/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9772 - loss: 0.0747 - val_accuracy: 0.9715 - val_loss: 0.1028\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.9771 - loss: 0.0740 - val_accuracy: 0.9722 - val_loss: 0.1025\n",
"Epoch 8/25\n", "Epoch 8/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9773 - loss: 0.0707 - val_accuracy: 0.9731 - val_loss: 0.0950\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9781 - loss: 0.0684 - val_accuracy: 0.9713 - val_loss: 0.0975\n",
"Epoch 9/25\n", "Epoch 9/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9801 - loss: 0.0605 - val_accuracy: 0.9747 - val_loss: 0.0953\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9814 - loss: 0.0592 - val_accuracy: 0.9726 - val_loss: 0.0988\n",
"Epoch 10/25\n", "Epoch 10/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9813 - loss: 0.0599 - val_accuracy: 0.9731 - val_loss: 0.0968\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9830 - loss: 0.0560 - val_accuracy: 0.9713 - val_loss: 0.1129\n",
"Epoch 11/25\n", "Epoch 11/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9836 - loss: 0.0521 - val_accuracy: 0.9738 - val_loss: 0.1034\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9831 - loss: 0.0554 - val_accuracy: 0.9734 - val_loss: 0.1071\n",
"Epoch 12/25\n", "Epoch 12/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9853 - loss: 0.0463 - val_accuracy: 0.9758 - val_loss: 0.0912\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9844 - loss: 0.0486 - val_accuracy: 0.9753 - val_loss: 0.0967\n",
"Epoch 13/25\n", "Epoch 13/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9847 - loss: 0.0494 - val_accuracy: 0.9744 - val_loss: 0.1016\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.9857 - loss: 0.0467 - val_accuracy: 0.9758 - val_loss: 0.0978\n",
"Epoch 14/25\n", "Epoch 14/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9859 - loss: 0.0459 - val_accuracy: 0.9751 - val_loss: 0.1031\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9857 - loss: 0.0456 - val_accuracy: 0.9728 - val_loss: 0.1028\n",
"Epoch 15/25\n", "Epoch 15/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 8ms/step - accuracy: 0.9854 - loss: 0.0456 - val_accuracy: 0.9742 - val_loss: 0.1089\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.9866 - loss: 0.0438 - val_accuracy: 0.9742 - val_loss: 0.1067\n",
"Epoch 16/25\n", "Epoch 16/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9874 - loss: 0.0435 - val_accuracy: 0.9772 - val_loss: 0.0990\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 10ms/step - accuracy: 0.9861 - loss: 0.0439 - val_accuracy: 0.9746 - val_loss: 0.0986\n",
"Epoch 17/25\n", "Epoch 17/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9877 - loss: 0.0401 - val_accuracy: 0.9752 - val_loss: 0.1109\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 11ms/step - accuracy: 0.9865 - loss: 0.0433 - val_accuracy: 0.9766 - val_loss: 0.1007\n",
"Epoch 18/25\n", "Epoch 18/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9891 - loss: 0.0346 - val_accuracy: 0.9747 - val_loss: 0.1082\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9891 - loss: 0.0366 - val_accuracy: 0.9764 - val_loss: 0.1119\n",
"Epoch 19/25\n", "Epoch 19/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9882 - loss: 0.0383 - val_accuracy: 0.9775 - val_loss: 0.1048\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9884 - loss: 0.0371 - val_accuracy: 0.9751 - val_loss: 0.1098\n",
"Epoch 20/25\n", "Epoch 20/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9897 - loss: 0.0325 - val_accuracy: 0.9778 - val_loss: 0.1025\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9894 - loss: 0.0352 - val_accuracy: 0.9758 - val_loss: 0.1167\n",
"Epoch 21/25\n", "Epoch 21/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9895 - loss: 0.0346 - val_accuracy: 0.9768 - val_loss: 0.1113\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9894 - loss: 0.0347 - val_accuracy: 0.9756 - val_loss: 0.1151\n",
"Epoch 22/25\n", "Epoch 22/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9899 - loss: 0.0338 - val_accuracy: 0.9743 - val_loss: 0.1128\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9891 - loss: 0.0357 - val_accuracy: 0.9738 - val_loss: 0.1123\n",
"Epoch 23/25\n", "Epoch 23/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9907 - loss: 0.0305 - val_accuracy: 0.9769 - val_loss: 0.1209\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9904 - loss: 0.0300 - val_accuracy: 0.9753 - val_loss: 0.1220\n",
"Epoch 24/25\n", "Epoch 24/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9893 - loss: 0.0349 - val_accuracy: 0.9753 - val_loss: 0.1185\n", "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.9894 - loss: 0.0355 - val_accuracy: 0.9759 - val_loss: 0.1151\n",
"Epoch 25/25\n", "Epoch 25/25\n",
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.9909 - loss: 0.0311 - val_accuracy: 0.9760 - val_loss: 0.1198\n" "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9913 - loss: 0.0292 - val_accuracy: 0.9764 - val_loss: 0.1148\n"
] ]
}, },
{ {
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"text/plain": [ "text/plain": [
"<keras.src.callbacks.history.History at 0x70c8450faf60>" "<keras.src.callbacks.history.History at 0x7f4486a77620>"
] ]
}, },
"execution_count": 94, "execution_count": 39,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -421,7 +436,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 95, "execution_count": 40,
"id": "da23d88f-8187-41eb-919b-c925b3330924", "id": "da23d88f-8187-41eb-919b-c925b3330924",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -429,8 +444,8 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9767 - loss: 0.1173\n", "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9766 - loss: 0.1092\n",
"Test accuracy: 0.9783999919891357\n" "Test accuracy: 0.9790999889373779\n"
] ]
} }
], ],
@ -1907,9 +1922,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "cd20cae1-be7f-49ee-b112-15f4cd9cfc6f", "id": "cd20cae1-be7f-49ee-b112-15f4cd9cfc6f",
"metadata": { "metadata": {},
"jp-MarkdownHeadingCollapsed": true
},
"source": [ "source": [
"# CNN - Convolutional Neural Network\n", "# CNN - Convolutional Neural Network\n",
"## zalety \n", "## zalety \n",
@ -1920,7 +1933,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 31,
"id": "0c993d2e-5730-4084-a3c1-1bc001041ba1", "id": "0c993d2e-5730-4084-a3c1-1bc001041ba1",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -1938,7 +1951,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 41,
"id": "2ee7ebc4-4be9-4720-b605-abe3153477dd", "id": "2ee7ebc4-4be9-4720-b605-abe3153477dd",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -1950,7 +1963,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 42,
"id": "8f6fd327-3219-4ad5-a5f6-c043ccd108ea", "id": "8f6fd327-3219-4ad5-a5f6-c043ccd108ea",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -1971,7 +1984,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 43,
"id": "1184eeaf-1cac-4a3f-b37d-b8edc3af40bd", "id": "1184eeaf-1cac-4a3f-b37d-b8edc3af40bd",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -1990,19 +2003,10 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 44,
"id": "56509616-c5fa-44ba-875c-5739e7411c60", "id": "56509616-c5fa-44ba-875c-5739e7411c60",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sasza/.local/share/virtualenvs/JupyterLab-9JRWupKp/lib/python3.12/site-packages/keras/src/layers/convolutional/base_conv.py:107: 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"
]
}
],
"source": [ "source": [
"model = Sequential(layers=[\n", "model = Sequential(layers=[\n",
" # blok 1\n", " # blok 1\n",
@ -2043,7 +2047,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 33, "execution_count": 45,
"id": "4d837958-3e5e-45a5-8d6c-33b2d7e1528b", "id": "4d837958-3e5e-45a5-8d6c-33b2d7e1528b",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -2057,18 +2061,18 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 46,
"id": "04b45a8e-58b2-43d8-8cf9-2fd86e961eb5", "id": "04b45a8e-58b2-43d8-8cf9-2fd86e961eb5",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n", "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
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@ -2134,9 +2138,9 @@
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n", "│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
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"│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n", "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">20,490</span> │\n", "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">20,490</span> │\n",
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"</pre>\n" "</pre>\n"
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@ -2198,9 +2202,9 @@
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"│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
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"│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m20,490\u001b[0m │\n", "│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m20,490\u001b[0m │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n" "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
] ]
}, },
@ -2261,7 +2265,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 47,
"id": "227d98ed-a60d-4fcf-aa0a-2f9fc1b4050e", "id": "227d98ed-a60d-4fcf-aa0a-2f9fc1b4050e",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -2270,32 +2274,32 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Epoch 1/5\n", "Epoch 1/5\n",
"\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 222ms/step - accuracy: 0.1819 - loss: 2.8103 - val_accuracy: 0.1018 - val_loss: 97.6675\n", "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 193ms/step - accuracy: 0.1846 - loss: 2.9937 - val_accuracy: 0.1139 - val_loss: 119.8130\n",
"Epoch 2/5\n", "Epoch 2/5\n",
"\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 222ms/step - accuracy: 0.2692 - loss: 2.1237 - val_accuracy: 0.1161 - val_loss: 82.1247\n", "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 194ms/step - accuracy: 0.2884 - loss: 2.0893 - val_accuracy: 0.0997 - val_loss: 174.2058\n",
"Epoch 3/5\n", "Epoch 3/5\n",
"\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 248ms/step - accuracy: 0.3134 - loss: 1.9531 - val_accuracy: 0.1122 - val_loss: 135.0129\n", "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 174ms/step - accuracy: 0.3147 - loss: 1.9001 - val_accuracy: 0.1000 - val_loss: 198.8870\n",
"Epoch 4/5\n", "Epoch 4/5\n",
"\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 226ms/step - accuracy: 0.3663 - loss: 1.7459 - val_accuracy: 0.1282 - val_loss: 175.1613\n", "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 181ms/step - accuracy: 0.3143 - loss: 1.8317 - val_accuracy: 0.1010 - val_loss: 159.6765\n",
"Epoch 5/5\n", "Epoch 5/5\n",
"\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 204ms/step - accuracy: 0.3564 - loss: 1.7099 - val_accuracy: 0.1055 - val_loss: 170.8446\n" "\u001b[1m195/195\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 206ms/step - accuracy: 0.3589 - loss: 1.7084 - val_accuracy: 0.1327 - val_loss: 79.0597\n"
] ]
}, },
{ {
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"<keras.src.callbacks.history.History at 0x7732e8e355e0>" "<keras.src.callbacks.history.History at 0x7f44840df1a0>"
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}, },
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"source": [ "source": [
"model.fit(\n", "model.fit(\n",
" x=data_generator.flow(X_train, Y_train, batch_size=16),\n", " x=data_generator.flow(X_train, Y_train, batch_size=256),\n",
" epochs=5,\n", " epochs=100,\n",
" verbose=1,\n", " verbose=1,\n",
" validation_data=(X_validation, Y_validation),\n", " validation_data=(X_validation, Y_validation),\n",
" steps_per_epoch=len(X_train) // 256\n", " steps_per_epoch=len(X_train) // 256\n",
@ -2737,7 +2741,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 19,
"id": "9bde4f30-f26c-4b37-bfa6-8b64460fe13d", "id": "9bde4f30-f26c-4b37-bfa6-8b64460fe13d",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -2749,7 +2753,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 20,
"id": "d2e9da87-1912-466f-b902-e45130d8fd32", "id": "d2e9da87-1912-466f-b902-e45130d8fd32",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -2761,19 +2765,10 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 21,
"id": "88156e8c-3c2a-467e-a018-a09c0f292de0", "id": "88156e8c-3c2a-467e-a018-a09c0f292de0",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sasza/.local/share/virtualenvs/JupyterLab-9JRWupKp/lib/python3.12/site-packages/keras/src/layers/core/embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
" warnings.warn(\n"
]
}
],
"source": [ "source": [
"model = Sequential()\n", "model = Sequential()\n",
"model.add(Embedding(vocab_size, embedding_dim, input_length=sequence_length))\n", "model.add(Embedding(vocab_size, embedding_dim, input_length=sequence_length))\n",
@ -2783,9 +2778,129 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 22,
"id": "f976e119-8888-4316-a1b8-2352f74c42e8", "id": "f976e119-8888-4316-a1b8-2352f74c42e8",
"metadata": {}, "metadata": {},
"outputs": [
{
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ simple_rnn_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>) │ ? │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
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"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
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"│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
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"source": [
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b024476-a946-4672-b1e9-8bb0de26c8e8",
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from tensorflow.keras.preprocessing.text import Tokenizer\n",
"from tensorflow.keras.utils import to_categorical\n",
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "862e9746-1f60-4036-b007-13240b285e2f",
"metadata": {},
"outputs": [],
"source": [
"text = \"\"\"\n",
"Ala ma kota. Kot ma na imię Mruczek. Mruczek lubi mleko. Ala daje mu mleko każdego dnia.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "76e6afde-0194-4714-8b77-8a1876c5d128",
"metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": []
} }