Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

keras-solution #3

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

keras-solution #3

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
265 changes: 21 additions & 244 deletions lab-guided-regression-models-with-keras.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -855,16 +855,7 @@
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-08-14 17:38:10.878028: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
}
],
"outputs": [],
"source": [
"from tensorflow import keras"
]
Expand All @@ -889,8 +880,8 @@
"metadata": {},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense"
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense"
]
},
{
Expand Down Expand Up @@ -972,16 +963,7 @@
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/joaorochaemelo/code/IH/venv_ironhack/lib/python3.11/site-packages/keras/src/layers/core/dense.py:87: 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"
]
}
],
"outputs": [],
"source": [
"# build the model\n",
"model = regression_model()"
Expand Down Expand Up @@ -1011,7 +993,19 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
"\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
"\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
Expand All @@ -1030,224 +1024,7 @@
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"23/23 - 1s - 40ms/step - loss: 1589.7310\n",
"Epoch 2/100\n",
"23/23 - 0s - 2ms/step - loss: 1498.0045\n",
"Epoch 3/100\n",
"23/23 - 0s - 2ms/step - loss: 1356.2686\n",
"Epoch 4/100\n",
"23/23 - 0s - 2ms/step - loss: 1121.4917\n",
"Epoch 5/100\n",
"23/23 - 0s - 2ms/step - loss: 804.4485\n",
"Epoch 6/100\n",
"23/23 - 0s - 2ms/step - loss: 484.7380\n",
"Epoch 7/100\n",
"23/23 - 0s - 2ms/step - loss: 283.7404\n",
"Epoch 8/100\n",
"23/23 - 0s - 2ms/step - loss: 223.6398\n",
"Epoch 9/100\n",
"23/23 - 0s - 2ms/step - loss: 209.2920\n",
"Epoch 10/100\n",
"23/23 - 0s - 5ms/step - loss: 199.1528\n",
"Epoch 11/100\n",
"23/23 - 0s - 2ms/step - loss: 191.6396\n",
"Epoch 12/100\n",
"23/23 - 0s - 2ms/step - loss: 185.0003\n",
"Epoch 13/100\n",
"23/23 - 0s - 2ms/step - loss: 179.1767\n",
"Epoch 14/100\n",
"23/23 - 0s - 2ms/step - loss: 174.0735\n",
"Epoch 15/100\n",
"23/23 - 0s - 2ms/step - loss: 170.2730\n",
"Epoch 16/100\n",
"23/23 - 0s - 2ms/step - loss: 166.6689\n",
"Epoch 17/100\n",
"23/23 - 0s - 2ms/step - loss: 162.4221\n",
"Epoch 18/100\n",
"23/23 - 0s - 2ms/step - loss: 159.4540\n",
"Epoch 19/100\n",
"23/23 - 0s - 2ms/step - loss: 156.3569\n",
"Epoch 20/100\n",
"23/23 - 0s - 2ms/step - loss: 153.3033\n",
"Epoch 21/100\n",
"23/23 - 0s - 2ms/step - loss: 150.7955\n",
"Epoch 22/100\n",
"23/23 - 0s - 2ms/step - loss: 148.7971\n",
"Epoch 23/100\n",
"23/23 - 0s - 2ms/step - loss: 146.6381\n",
"Epoch 24/100\n",
"23/23 - 0s - 2ms/step - loss: 144.7416\n",
"Epoch 25/100\n",
"23/23 - 0s - 2ms/step - loss: 142.6251\n",
"Epoch 26/100\n",
"23/23 - 0s - 2ms/step - loss: 141.0247\n",
"Epoch 27/100\n",
"23/23 - 0s - 2ms/step - loss: 139.7123\n",
"Epoch 28/100\n",
"23/23 - 0s - 2ms/step - loss: 137.8783\n",
"Epoch 29/100\n",
"23/23 - 0s - 2ms/step - loss: 136.1062\n",
"Epoch 30/100\n",
"23/23 - 0s - 2ms/step - loss: 134.7343\n",
"Epoch 31/100\n",
"23/23 - 0s - 2ms/step - loss: 133.0127\n",
"Epoch 32/100\n",
"23/23 - 0s - 2ms/step - loss: 131.7408\n",
"Epoch 33/100\n",
"23/23 - 0s - 2ms/step - loss: 130.2917\n",
"Epoch 34/100\n",
"23/23 - 0s - 2ms/step - loss: 128.9789\n",
"Epoch 35/100\n",
"23/23 - 0s - 2ms/step - loss: 128.0439\n",
"Epoch 36/100\n",
"23/23 - 0s - 2ms/step - loss: 127.1930\n",
"Epoch 37/100\n",
"23/23 - 0s - 2ms/step - loss: 125.3434\n",
"Epoch 38/100\n",
"23/23 - 0s - 2ms/step - loss: 123.9078\n",
"Epoch 39/100\n",
"23/23 - 0s - 2ms/step - loss: 122.7656\n",
"Epoch 40/100\n",
"23/23 - 0s - 2ms/step - loss: 121.3873\n",
"Epoch 41/100\n",
"23/23 - 0s - 2ms/step - loss: 120.2595\n",
"Epoch 42/100\n",
"23/23 - 0s - 2ms/step - loss: 119.1386\n",
"Epoch 43/100\n",
"23/23 - 0s - 2ms/step - loss: 117.5028\n",
"Epoch 44/100\n",
"23/23 - 0s - 2ms/step - loss: 116.4576\n",
"Epoch 45/100\n",
"23/23 - 0s - 2ms/step - loss: 114.6131\n",
"Epoch 46/100\n",
"23/23 - 0s - 2ms/step - loss: 113.0364\n",
"Epoch 47/100\n",
"23/23 - 0s - 2ms/step - loss: 111.7229\n",
"Epoch 48/100\n",
"23/23 - 0s - 2ms/step - loss: 110.1791\n",
"Epoch 49/100\n",
"23/23 - 0s - 2ms/step - loss: 108.3175\n",
"Epoch 50/100\n",
"23/23 - 0s - 2ms/step - loss: 107.3923\n",
"Epoch 51/100\n",
"23/23 - 0s - 2ms/step - loss: 105.5512\n",
"Epoch 52/100\n",
"23/23 - 0s - 5ms/step - loss: 103.5289\n",
"Epoch 53/100\n",
"23/23 - 0s - 2ms/step - loss: 101.4534\n",
"Epoch 54/100\n",
"23/23 - 0s - 2ms/step - loss: 100.1987\n",
"Epoch 55/100\n",
"23/23 - 0s - 2ms/step - loss: 99.1770\n",
"Epoch 56/100\n",
"23/23 - 0s - 2ms/step - loss: 97.6906\n",
"Epoch 57/100\n",
"23/23 - 0s - 2ms/step - loss: 94.2602\n",
"Epoch 58/100\n",
"23/23 - 0s - 2ms/step - loss: 92.5050\n",
"Epoch 59/100\n",
"23/23 - 0s - 2ms/step - loss: 89.8930\n",
"Epoch 60/100\n",
"23/23 - 0s - 2ms/step - loss: 87.6395\n",
"Epoch 61/100\n",
"23/23 - 0s - 2ms/step - loss: 85.7578\n",
"Epoch 62/100\n",
"23/23 - 0s - 2ms/step - loss: 83.9698\n",
"Epoch 63/100\n",
"23/23 - 0s - 2ms/step - loss: 81.4788\n",
"Epoch 64/100\n",
"23/23 - 0s - 2ms/step - loss: 78.8132\n",
"Epoch 65/100\n",
"23/23 - 0s - 2ms/step - loss: 76.6305\n",
"Epoch 66/100\n",
"23/23 - 0s - 2ms/step - loss: 74.4165\n",
"Epoch 67/100\n",
"23/23 - 0s - 2ms/step - loss: 72.1750\n",
"Epoch 68/100\n",
"23/23 - 0s - 2ms/step - loss: 70.6617\n",
"Epoch 69/100\n",
"23/23 - 0s - 2ms/step - loss: 68.0864\n",
"Epoch 70/100\n",
"23/23 - 0s - 2ms/step - loss: 66.9356\n",
"Epoch 71/100\n",
"23/23 - 0s - 2ms/step - loss: 64.2999\n",
"Epoch 72/100\n",
"23/23 - 0s - 2ms/step - loss: 62.2969\n",
"Epoch 73/100\n",
"23/23 - 0s - 2ms/step - loss: 61.3270\n",
"Epoch 74/100\n",
"23/23 - 0s - 2ms/step - loss: 60.2607\n",
"Epoch 75/100\n",
"23/23 - 0s - 2ms/step - loss: 57.8469\n",
"Epoch 76/100\n",
"23/23 - 0s - 2ms/step - loss: 56.7945\n",
"Epoch 77/100\n",
"23/23 - 0s - 2ms/step - loss: 55.5016\n",
"Epoch 78/100\n",
"23/23 - 0s - 2ms/step - loss: 54.0605\n",
"Epoch 79/100\n",
"23/23 - 0s - 2ms/step - loss: 52.8370\n",
"Epoch 80/100\n",
"23/23 - 0s - 2ms/step - loss: 51.5477\n",
"Epoch 81/100\n",
"23/23 - 0s - 2ms/step - loss: 50.3530\n",
"Epoch 82/100\n",
"23/23 - 0s - 2ms/step - loss: 50.2427\n",
"Epoch 83/100\n",
"23/23 - 0s - 2ms/step - loss: 48.8843\n",
"Epoch 84/100\n",
"23/23 - 0s - 2ms/step - loss: 47.7792\n",
"Epoch 85/100\n",
"23/23 - 0s - 2ms/step - loss: 47.3964\n",
"Epoch 86/100\n",
"23/23 - 0s - 2ms/step - loss: 46.3647\n",
"Epoch 87/100\n",
"23/23 - 0s - 2ms/step - loss: 45.4960\n",
"Epoch 88/100\n",
"23/23 - 0s - 2ms/step - loss: 44.3812\n",
"Epoch 89/100\n",
"23/23 - 0s - 2ms/step - loss: 43.8020\n",
"Epoch 90/100\n",
"23/23 - 0s - 2ms/step - loss: 43.1113\n",
"Epoch 91/100\n",
"23/23 - 0s - 2ms/step - loss: 42.5655\n",
"Epoch 92/100\n",
"23/23 - 0s - 2ms/step - loss: 41.5551\n",
"Epoch 93/100\n",
"23/23 - 0s - 2ms/step - loss: 41.3190\n",
"Epoch 94/100\n",
"23/23 - 0s - 2ms/step - loss: 41.0615\n",
"Epoch 95/100\n",
"23/23 - 0s - 5ms/step - loss: 40.1528\n",
"Epoch 96/100\n",
"23/23 - 0s - 2ms/step - loss: 39.6206\n",
"Epoch 97/100\n",
"23/23 - 0s - 2ms/step - loss: 38.8477\n",
"Epoch 98/100\n",
"23/23 - 0s - 2ms/step - loss: 39.0186\n",
"Epoch 99/100\n",
"23/23 - 0s - 2ms/step - loss: 38.2057\n",
"Epoch 100/100\n",
"23/23 - 0s - 2ms/step - loss: 37.8294\n"
]
},
{
"data": {
"text/plain": [
"<keras.src.callbacks.history.History at 0x1411f6050>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"# fit the model\n",
"model.fit(X_train, y_train, epochs=100, verbose=2)"
Expand All @@ -1268,7 +1045,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -1293,7 +1070,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -1365,7 +1142,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
"version": "3.8.15"
}
},
"nbformat": 4,
Expand Down