From e91dc7742f04667c3861502167a352cba12f17dd Mon Sep 17 00:00:00 2001 From: rezayaghobi-dev Date: Sat, 11 Jul 2026 22:01:14 +0330 Subject: [PATCH] Initial commit: MLP regularization, CNN BatchNorm comparison, autoencoder denoising --- .gitignore | 20 + README.md | 28 ++ autoencoder-denoising/README.md | 33 ++ .../autoencoder-denoising-result.png | Bin 0 -> 42580 bytes .../encoder-noisy-image.ipynb | 445 +++++++++++++++++ cnn-batchnorm-comparison/README.md | 33 ++ cnn-batchnorm-comparison/conv-pred-BN.ipynb | 448 ++++++++++++++++++ cnn-batchnorm-comparison/conv-pred.ipynb | 431 +++++++++++++++++ docs/Autoencoder-Denoising.md | 19 + docs/CNN-BatchNorm.md | 31 ++ docs/Home.md | 9 + docs/MLP-Regularization.md | 41 ++ docs/images/autoencoder-denoising-result.png | Bin 0 -> 42580 bytes docs/images/cnn-baseline-loss.png | Bin 0 -> 24223 bytes docs/images/cnn-bn-loss.png | Bin 0 -> 20001 bytes docs/images/mlp-baseline-loss.png | Bin 0 -> 24182 bytes docs/images/mlp-baseline-misclassified.png | Bin 0 -> 13228 bytes docs/images/mlp-dropout-loss.png | Bin 0 -> 20657 bytes docs/images/mlp-dropout-misclassified.png | Bin 0 -> 12768 bytes mnist-mlp-regularization/README.md | 40 ++ .../mnist-mlp-pred-dropout.ipynb | 443 +++++++++++++++++ mnist-mlp-regularization/mnist-mlp-pred.ipynb | 436 +++++++++++++++++ 22 files changed, 2457 insertions(+) create mode 100644 .gitignore create mode 100644 README.md create mode 100644 autoencoder-denoising/README.md create mode 100644 autoencoder-denoising/autoencoder-denoising-result.png create mode 100644 autoencoder-denoising/encoder-noisy-image.ipynb create mode 100644 cnn-batchnorm-comparison/README.md create mode 100644 cnn-batchnorm-comparison/conv-pred-BN.ipynb create mode 100644 cnn-batchnorm-comparison/conv-pred.ipynb create mode 100644 docs/Autoencoder-Denoising.md create mode 100644 docs/CNN-BatchNorm.md create mode 100644 docs/Home.md create mode 100644 docs/MLP-Regularization.md create mode 100644 docs/images/autoencoder-denoising-result.png create mode 100644 docs/images/cnn-baseline-loss.png create mode 100644 docs/images/cnn-bn-loss.png create mode 100644 docs/images/mlp-baseline-loss.png create mode 100644 docs/images/mlp-baseline-misclassified.png create mode 100644 docs/images/mlp-dropout-loss.png create mode 100644 docs/images/mlp-dropout-misclassified.png create mode 100644 mnist-mlp-regularization/README.md create mode 100644 mnist-mlp-regularization/mnist-mlp-pred-dropout.ipynb create mode 100644 mnist-mlp-regularization/mnist-mlp-pred.ipynb diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..6c455f9 --- /dev/null +++ b/.gitignore @@ -0,0 +1,20 @@ +# Python +__pycache__/ +*.py[cod] +.venv/ +venv/ + +# Jupyter +.ipynb_checkpoints/ + +# Datasets and model artifacts โ€” regeneratable, don't belong in git +datasets/ +*.h5 +*.keras +*.pb +*.ckpt +*.weights + +# OS / editor +.DS_Store +.vscode/ diff --git a/README.md b/README.md new file mode 100644 index 0000000..007fc32 --- /dev/null +++ b/README.md @@ -0,0 +1,28 @@ +# Deep Learning Portfolio + +A collection of deep learning experiments built with TensorFlow/Keras โ€” regularization and normalization comparisons, and an autoencoder for image denoising. + +![TensorFlow](https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white) +![Keras](https://img.shields.io/badge/Keras-D00000?style=flat&logo=keras&logoColor=white) +![Python](https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white) + +๐Ÿ“– **Full write-ups with result plots:** [docs/Home.md](docs/Home.md) + +## Projects + +| Project | Comparison | Result | +|---|---|---| +| [mnist-mlp-regularization](mnist-mlp-regularization/) | Baseline MLP vs Dropout | Test loss 0.279 โ†’ 0.099 *(not a clean ablation โ€” see project README)* | +| [cnn-batchnorm-comparison](cnn-batchnorm-comparison/) | Baseline CNN vs BatchNorm | Test loss 0.289 โ†’ 0.094 (clean ablation) | +| [autoencoder-denoising](autoencoder-denoising/) | Noisy โ†’ clean image reconstruction | Recognizable digits recovered through a 32-unit bottleneck | + +Each project folder has its own README with architecture, training config, and results. The `docs/` folder has deeper write-ups with the actual result plots. + +## Tech stack + +Python, TensorFlow, Keras, NumPy, matplotlib + +## Author + +**Reza** โ€” Computer Networks student, LPIC-1 +[GitHub](https://github.com/rezayaghobi-dev) diff --git a/autoencoder-denoising/README.md b/autoencoder-denoising/README.md new file mode 100644 index 0000000..eae6ab9 --- /dev/null +++ b/autoencoder-denoising/README.md @@ -0,0 +1,33 @@ +# Autoencoder Image Denoising โ€” MNIST + +A simple fully-connected autoencoder trained to reconstruct clean MNIST digits from noisy versions of them. + +## Architecture + +``` +Input(784) โ†’ Dense(32, relu) [encoder/bottleneck] + โ†’ Dense(784, sigmoid) [decoder] +``` + +Gaussian noise is added to the input images; the network is trained to output the *clean* version, using binary crossentropy loss between the reconstruction and the original image. + +## Approach + +1. Load MNIST, normalize pixel values to [0, 1] +2. Add Gaussian noise (factor 0.5) to create noisy training inputs +3. Train the autoencoder to map noisy โ†’ clean, compressing through a 32-unit bottleneck +4. Evaluate by visually comparing original, noisy, and reconstructed images + +## Result + +![Denoising result](autoencoder-denoising-result.png) + +Despite the bottleneck compressing each 784-pixel image down to just 32 values, the network recovers a recognizable digit โ€” showing the encoder is learning a meaningful compressed representation rather than just copying pixels through. + +## Tech stack + +TensorFlow / Keras, NumPy, matplotlib + +## Notebook + +[`encoder-noisy-image.ipynb`](encoder-noisy-image.ipynb) diff --git a/autoencoder-denoising/autoencoder-denoising-result.png b/autoencoder-denoising/autoencoder-denoising-result.png new file mode 100644 index 0000000000000000000000000000000000000000..877e941bc16b10bf134c3fa15f1f681bf889753f GIT binary patch literal 42580 zcma&O2UJt(x;O5enb8@sj13hjqu50d=_N3Z1Bg;o6eK`UBY_x-5kd)VM#qW?W1|OD zN~A^%0V#n&fv9u>2|YrT79d7aFcA2jaL&2^bJzOc@4Kw!f*>h-@ArM4UwihQlTLQ4 zSE{XCwrtsI`(wYIUbgJ}sb$N)IrhVH_)T+F*G~9m9%6GY#3cw55_aLL&oajgAy@o^ 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as plt \n", + "import numpy as np \n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3c3c5ee0-eb4d-4ad9-8d1f-42bd153d7fa9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60000, 28, 28)\n", + "(10000, 28, 28)\n" + ] + } + ], + "source": [ + "(X_train,_),(X_test,_) = mnist.load_data()\n", + "X_train= X_train.astype('float')/255\n", + "X_test = X_test.astype('float')/255\n", + "noise_factor = 0.5\n", + "\n", + "X_train_noisy = X_train + noise_factor * np.random.normal(loc= 0.0 ,scale=1.0, size=X_train.shape)\n", + "X_test_noisy = X_test+ noise_factor * np.random.normal(loc= 0.0 ,scale=1.0, size=X_test.shape)\n", + "\n", + "X_train_noisy= np.clip(X_train_noisy,0.,1.)\n", + "X_test_noisy= np.clip(X_test_noisy,0.,1.)\n", + "\n", + "print(X_train.shape)\n", + "print(X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "46fd7fed-d520-43b3-a13e-61b31219f8c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60000, 784)\n", + "(10000, 784)\n" + ] + } + ], + "source": [ + "X_train = X_train.reshape(len(X_train),784)\n", + "X_test =X_test.reshape(len(X_test),784)\n", + "\n", + "X_train_noisy = X_train_noisy.reshape(len(X_train),784)\n", + "X_test_noisy =X_test_noisy.reshape(len(X_test),784)\n", + "\n", + "print(X_train.shape)\n", + "print(X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "859ca52a-6459-42b6-87f2-9c71d6db83f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"functional_3\"\n",
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+       "โ”ƒ Layer (type)                         โ”ƒ Output Shape                โ”ƒ         Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ input_layer_2 (InputLayer)           โ”‚ (None, 784)                 โ”‚               0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_4 (Dense)                      โ”‚ (None, 32)                  โ”‚          25,120 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_5 (Dense)                      โ”‚ (None, 784)                 โ”‚          25,872 โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
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loss: 0.1957\n", + "Epoch 3/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1705\n", + "Epoch 4/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1567\n", + "Epoch 5/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1477\n", + "Epoch 6/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1411\n", + "Epoch 7/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1363\n", + "Epoch 8/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1324\n", + "Epoch 9/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1301\n", + "Epoch 10/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1287\n", + "Epoch 11/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1278\n", + "Epoch 12/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1273\n", + "Epoch 13/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1270\n", + "Epoch 14/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1264\n", + "Epoch 15/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1259\n", + "Epoch 16/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1257\n", + "Epoch 17/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1255\n", + "Epoch 18/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1253\n", + "Epoch 19/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1252\n", + "Epoch 20/30\n", + "\u001b[1m235/235\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - 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Model: \"functional_4\"\n",
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โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                         โ”ƒ Output Shape                โ”ƒ         Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ input_layer_2 (InputLayer)           โ”‚ (None, 784)                 โ”‚               0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_4 (Dense)                      โ”‚ (None, 32)                  โ”‚          25,120 โ”‚\n",
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(40,4))\n", + "for i in range(10): \n", + " #display original\n", + " ax = plt.subplot(3,20,i+1)\n", + " plt.imshow(X_test_noisy[i].reshape(28,28))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "\n", + " #display encoded image\n", + " ax = plt.subplot(3,20,i+1+20)\n", + " plt.imshow(encoded_images[i].reshape(8,4))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + " #display reconstruction\n", + " ax = plt.subplot(3 , 20 ,2 * 20 + i+ 1)\n", + " plt.imshow(pred[i].reshape(28,28))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "580c3e12-c29b-4040-a419-c2f194ac766e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/cnn-batchnorm-comparison/README.md b/cnn-batchnorm-comparison/README.md new file mode 100644 index 0000000..a4ad839 --- /dev/null +++ b/cnn-batchnorm-comparison/README.md @@ -0,0 +1,33 @@ +# CNN BatchNorm Comparison โ€” MNIST + +Comparing a baseline CNN against the same architecture with Batch Normalization added, on MNIST digit classification. + +## Architecture + +``` +Conv2D(32, 3x3, stride 2) โ†’ Conv2D(64, 3x3, stride 2) โ†’ Flatten โ†’ Dense(10, softmax) +``` + +The BatchNorm version inserts `BatchNormalization` + separate `Activation('relu')` after each conv layer, instead of the activation being baked into the conv layer directly. + +## Clean ablation + +Both models use the **identical optimizer and learning rate** (SGD, lr=0.001) โ€” Batch Normalization is the only variable that changed. This makes the result directly attributable to BatchNorm, unlike the [MLP regularization comparison](../mnist-mlp-regularization/README.md) in this repo where two variables changed at once. + +## Results + +| Model | Test loss (categorical crossentropy) | +|---|---| +| Baseline CNN | 0.289 | +| CNN + BatchNorm | 0.094 | + +BatchNorm alone (same optimizer, same learning rate) cut test loss by roughly two-thirds โ€” consistent with its known effect of stabilizing and accelerating convergence, especially under plain SGD at a conservative learning rate. + +## Tech stack + +TensorFlow / Keras, NumPy, matplotlib + +## Notebooks + +- [`conv-pred.ipynb`](conv-pred.ipynb) โ€” baseline +- [`conv-pred-BN.ipynb`](conv-pred-BN.ipynb) โ€” with BatchNorm diff --git a/cnn-batchnorm-comparison/conv-pred-BN.ipynb b/cnn-batchnorm-comparison/conv-pred-BN.ipynb new file mode 100644 index 0000000..084da17 --- /dev/null +++ b/cnn-batchnorm-comparison/conv-pred-BN.ipynb @@ -0,0 +1,448 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "449ccd64-ec1a-46b6-9e95-cb03f4d01296", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import datasets,utils,models,layers,optimizers,losses\n", + "import numpy as np \n", + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "markdown", + "id": "06e6b0ab-0b31-4ade-ac9f-4e1d37de2c85", + "metadata": {}, + "source": [ + "# data Prepration " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e70e5895-f688-4b36-8ee3-3e9fa77433c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "(train_images , train_labels), (test_images,test_labels ) = datasets.mnist.load_data()\n", + "\n", + "# preprocessing\n", + "X_train = train_images.reshape(60000,28,28,1)\n", + "X_test = test_images.reshape(10000,28,28,1)\n", + "\n", + "# Normalizing (0,1 )\n", + "X_train = X_train.astype('float') / 255\n", + "X_test = X_test.astype('float') / 255\n", + "\n", + "y_train = utils.to_categorical(train_labels)\n", + "y_test = utils.to_categorical(test_labels) \n", + "\n", + "print(train_labels[0])\n", + "print(y_train[0])\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "0d73c52c-40f2-4a37-a289-5bd24f24cec7", + "metadata": {}, + "source": [ + "# model creation " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e33a3d4a-7f5d-4763-b3c4-ede9a74b484a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"functional_1\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"functional_1\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                         โ”ƒ Output Shape                โ”ƒ         Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ input_layer_4 (InputLayer)           โ”‚ (None, 28, 28, 1)           โ”‚               0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ conv2d_5 (Conv2D)                    โ”‚ (None, 14, 14, 32)          โ”‚             320 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ batch_normalization_5                โ”‚ (None, 14, 14, 32)          โ”‚             128 โ”‚\n",
+       "โ”‚ (BatchNormalization)                 โ”‚                             โ”‚                 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ activation_2 (Activation)            โ”‚ (None, 14, 14, 32)          โ”‚               0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ conv2d_6 (Conv2D)                    โ”‚ (None, 7, 7, 64)            โ”‚          18,496 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ batch_normalization_6                โ”‚ (None, 7, 7, 64)            โ”‚             256 โ”‚\n",
+       "โ”‚ (BatchNormalization)                 โ”‚                             โ”‚                 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ activation_3 (Activation)            โ”‚ (None, 7, 7, 64)            โ”‚               0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ flatten_1 (Flatten)                  โ”‚ (None, 3136)                โ”‚               0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_1 (Dense)                      โ”‚ (None, 10)                  โ”‚          31,370 โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ input_layer_4 (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m320\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ batch_normalization_5 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m128\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalization\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ activation_2 (\u001b[38;5;33mActivation\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m18,496\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ batch_normalization_6 โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m256\u001b[0m โ”‚\n", + "โ”‚ (\u001b[38;5;33mBatchNormalization\u001b[0m) โ”‚ โ”‚ โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ activation_3 (\u001b[38;5;33mActivation\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3136\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_1 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m31,370\u001b[0m โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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 Trainable params: 50,378 (196.79 KB)\n",
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 Non-trainable params: 192 (768.00 B)\n",
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"49ed788e-2c4e-4a77-9c86-6da3d1dcd089", + "metadata": {}, + "source": [ + "# Train and Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "042a17cd-f9c0-4eea-975c-bc1bd4257a90", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 11ms/step - loss: 1.1879 - val_loss: 1.2940\n", + "Epoch 2/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.5931 - val_loss: 0.4890\n", + "Epoch 3/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.4617 - val_loss: 0.3935\n", + "Epoch 4/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.3955 - val_loss: 0.3476\n", + "Epoch 5/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.3528 - val_loss: 0.3151\n", + "Epoch 6/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.3217 - val_loss: 0.2909\n", + "Epoch 7/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.2972 - val_loss: 0.2712\n", + "Epoch 8/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.2775 - val_loss: 0.2564\n", + "Epoch 9/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.2610 - val_loss: 0.2423\n", + "Epoch 10/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.2469 - val_loss: 0.2302\n", + "Epoch 11/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.2345 - val_loss: 0.2196\n", + "Epoch 12/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.2236 - val_loss: 0.2103\n", + "Epoch 13/50\n", + "\u001b[1m375/375\u001b[0m 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loss: 0.1035 - val_loss: 0.1090\n", + "Epoch 45/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.1020 - val_loss: 0.1080\n", + "Epoch 46/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.1007 - val_loss: 0.1066\n", + "Epoch 47/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.0992 - val_loss: 0.1056\n", + "Epoch 48/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.0980 - val_loss: 0.1044\n", + "Epoch 49/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.0966 - val_loss: 0.1033\n", + "Epoch 50/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - loss: 0.0953 - val_loss: 0.1022\n" + ] + } + ], + "source": [ + "history = mymodel.fit(X_train,y_train,batch_size=128,epochs=50,validation_split=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "60822a89-5a77-4b5d-85e0-592889588a2e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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gW8oI+SAcSnJFIiIibY/CTQxZLAYWZ8aBHRpULCIiknAKNzFmd7gJm/VLtatrSkREJOEUbmIsw20/MGNKg4pFREQSTuEmxhrNmFLLjYiISMIp3MTYwTOmNOZGREQk8RRuYizSctNwIz+FGxERkURTuImxyF2KG27kp3AjIiKSaAo3MZbhtB805kbhRkREJNEUbmIsw2WjzlS3lIiISLIo3MRYptNGrWZLiYiIJI3CTYxluGx41S0lIiKSNAo3MZbhtFFrakCxiIhIsijcxFjGwbOl1HIjIiKScAo3MZbpVLgRERFJJoWbGMt02Q+aLaUBxSIiIommcBNjjbqltHCmiIhIwincxFjGQVPBQxpQLCIiknAKNzGW4bThre+WCvvUciMiIpJoCjcxZrUYhGxpAITVciMiIpJwCjdxYNjdAJgKNyIiIgmncBMPzvTIo6aCi4iIJJzCTRxYHZFuKSOoqeAiIiKJpnATBzZXBgDWoFpuREREEk3hJg5s9d1SlqAXTDPJ1YiIiLQtCjdx4HDXhxvCEPQluRoREZG2ReEmDhzujAMvNKhYREQkoRRu4iDd7cJvWiMvtL6UiIhIQincxEGGVgYXERFJGoWbOGi0eKbCjYiISEIlNdwsWrSIsWPHUlRUhGEYvPzyy9/6noULFzJ48GBcLhfdu3fnL3/5S/wLPUqZLju1ZsPK4Ao3IiIiiZTUcFNTU8PAgQN59NFHj+j8jRs3cvHFF3P22WezatUqfvWrX3HHHXfwwgsvxLnSo5PptOFVy42IiEhS2JL55WPGjGHMmDFHfP5f/vIXunTpwsyZMwHo06cPy5cv5/e//z3jxo1r9j0+nw+f78B0bI/Hc1w1H4kMl41ahRsREZGkaFVjbj788ENGjx7daN93vvMdli9fTiAQaPY9M2bMIDs7O7oVFxfHvc4Mp4060xF5odlSIiIiCdWqws2OHTsoLCxstK+wsJBgMMju3bubfc/06dOprKyMbuXl5XGvM+Pgbil/Tdy/T0RERA5IarfUsTAMo9Frs355g0P3N3A6nTidzrjXdbDMg7qlQv5arAn9dhERkbatVbXcdOjQgR07djTaV1FRgc1mIy8vL0lVNZV+ULeUv646ydWIiIi0La0q3AwbNowFCxY02vf2228zZMgQ7HZ7kqpqym614Le4AAgo3IiIiCRUUsNNdXU1paWllJaWApGp3qWlpZSVlQGR8TLjx4+Pnj958mQ2b97MtGnTWLduHU8//TRPPfUUP/3pT5NRfovCNjcAAZ/G3IiIiCRSUsfcLF++nPPPPz/6etq0aQBMmDCBZ555hu3bt0eDDkBJSQmvv/46d911F4899hhFRUX86U9/Ouw08GQK29LADyGvwo2IiEgiJTXcnHfeedEBwc155plnmuw799xzWblyZRyrig3T7gY/hHWHYhERkYRqVWNuWhV7pFvK1FRwERGRhFK4iRPDng6AqZv4iYiIJJTCTZxYnGkAGFp+QUREJKEUbuLE6oy03FiCarkRERFJJIWbOLG5IuHGqnAjIiKSUAo3cWJ3ZwBgCynciIiIJJLCTZw4XPXhJuxNciUiIiJti8JNnLjSIuHGoXAjIiKSUAo3cRINNwQgHEpyNSIiIm2Hwk2cuNMyD7zQdHAREZGEUbiJk7T0jAMvdCM/ERGRhFG4iZNMl4Na0xl5oZYbERGRhFG4iZMMl41aIuEm5NP6UiIiIomicBMnGU4bXhwA1NVUJ7kaERGRtkPhJk4cNgt1uACoq/EkuRoREZG2Q+EmjvxGpFvKW6eWGxERkURRuImjgCXScuOvrUpyJSIiIm2Hwk0cBa314carAcUiIiKJonATRyGrG4CgV91SIiIiiaJwE0chW3248ek+NyIiIomicBNHZn24CSvciIiIJIzCTTw50gEI+zXmRkREJFEUbuLIsEdabky/Wm5EREQSReEmjgxnpOXG0NpSIiIiCaNwE0dWRxoARlCrgouIiCSKwk0c2VwZAFgVbkRERBJG4SaObK5It5Q1pHAjIiKSKAo3ceSob7mxh7xJrkRERKTtULiJI2dafbgxFW5EREQSReEmjlz14cYR9iW5EhERkbZD4SaO3OmZkUe8hMJmkqsRERFpGxRu4qgh3LjwU+MPJrkaERGRtkHhJo6c7ki4ScNLdV0gydWIiIi0DQo38VS//ILVMKmp012KRUREEkHhJp7sadGnNdVVSSxERESk7VC4iSernQA2AOpqFW5EREQSQeEmzvyGEwBfTXWSKxEREWkbFG7izG9xAeBVy42IiEhCKNzEWbA+3AS8NUmuREREpG1QuImzoDUyY8pfp24pERGRRFC4ibOQLRJuQj613IiIiCSCwk2cmbbIdHCFGxERkcRQuIkzs/5GfmG/buInIiKSCAo3cWbUhxsUbkRERBJC4SbODGd65ElA4UZERCQRkh5uZs2aRUlJCS6Xi8GDB7N48eIWz587dy4DBw4kLS2Njh07cuONN7Jnz54EVXv0rI76JRgCdcktREREpI1IariZN28eU6dO5e6772bVqlWcffbZjBkzhrKysmbP/+CDDxg/fjw33XQTa9asYf78+XzyySdMmjQpwZUfOasrI/IYUsuNiIhIIiQ13Dz88MPcdNNNTJo0iT59+jBz5kyKi4uZPXt2s+cvW7aMbt26cccdd1BSUsJZZ53FLbfcwvLlyw/7HT6fD4/H02hLJLsr0i1lDXoT+r0iIiJtVdLCjd/vZ8WKFYwePbrR/tGjR7N06dJm3zN8+HC2bNnC66+/jmma7Ny5k3/9619ccsklh/2eGTNmkJ2dHd2Ki4tj+nN8G3t9y409XEc4bCb0u0VERNqipIWb3bt3EwqFKCwsbLS/sLCQHTt2NPue4cOHM3fuXK655hocDgcdOnQgJyeHP//5z4f9nunTp1NZWRndysvLY/pzfBunOxJuXPipDYQS+t0iIiJtUdIHFBuG0ei1aZpN9jVYu3Ytd9xxB/fccw8rVqzgzTffZOPGjUyePPmwn+90OsnKymq0JZKtvlsqDS/V3mBCv1tERKQtsiXri/Pz87FarU1aaSoqKpq05jSYMWMGI0aM4Gc/+xkAp5xyCunp6Zx99tk8+OCDdOzYMe51Hy2jfraUy/BT7QsAruQWJCIikuKS1nLjcDgYPHgwCxYsaLR/wYIFDB8+vNn31NbWYrE0LtlqtQKRFp8Tkj0SbtLwUaWWGxERkbhLarfUtGnTePLJJ3n66adZt24dd911F2VlZdFupunTpzN+/Pjo+WPHjuXFF19k9uzZbNiwgSVLlnDHHXdwxhlnUFRUlKwfo2X14catcCMiIpIQSeuWArjmmmvYs2cPDzzwANu3b6d///68/vrrdO3aFYDt27c3uufNxIkTqaqq4tFHH+UnP/kJOTk5XHDBBfz3f/93sn6Eb1e//EKkW0rhRkREJN4M84Ttz4kPj8dDdnY2lZWViRlcvOtLeOwM9pkZLLh0GVefntip6CIiIqngaP5+J322VMqrb7lx46NKLTciIiJxp3ATb/bIVHCXEaCmzpfkYkRERFKfwk281bfcAHjrqpNYiIiISNugcBNvB4WbgMKNiIhI3CncxJthELBEbtzn99YkuRgREZHUp3CTAGFbpPVGLTciIiLxp3CTAGFb5EZ+IX9tkisRERFJfQo3CWDWj7sJ+xRuRERE4k3hJhHql2Aw/RpzIyIiEm8KNwnQsDK4GVDLjYiISLwp3CSAxRG5kZ8lUHvirl4uIiKSIhRuEsDqjLTcOPFTFwgluRoREZHUpnCTAFZXpOUmDS9VXq0vJSIiEk/HFG6effZZXnvttejrn//85+Tk5DB8+HA2b94cs+JShVE/oNiNX+FGREQkzo4p3Dz00EO43ZHpzR9++CGPPvoov/vd78jPz+euu+6KaYEpoSHcGH6qtTK4iIhIXNmO5U3l5eX07NkTgJdffpnvfe973HzzzYwYMYLzzjsvlvWlhmjLjZdqtdyIiIjE1TG13GRkZLBnzx4A3n77bUaNGgWAy+Wirq4udtWlivqb+LnxU+0LJLkYERGR1HZMLTcXXnghkyZNYtCgQXz11VdccsklAKxZs4Zu3brFsr7UUD8VPM3wacyNiIhInB1Ty81jjz3GsGHD2LVrFy+88AJ5eXkArFixgmuvvTamBaaE+pYbFz6NuREREYmzY2q5ycnJ4dFHH22y//777z/uglJS/ZibNHwacyMiIhJnx9Ry8+abb/LBBx9EXz/22GOceuqpXHfddezbty9mxaWMg2ZLVanlRkREJK6OKdz87Gc/w+PxALB69Wp+8pOfcPHFF7NhwwamTZsW0wJTgqNhtpTG3IiIiMTbMXVLbdy4kb59+wLwwgsvcOmll/LQQw+xcuVKLr744pgWmBLsB8KNxtyIiIjE1zG13DgcDmprIytcv/POO4wePRqA3NzcaIuOHKRhKrjhp9qrqeAiIiLxdEwtN2eddRbTpk1jxIgRfPzxx8ybNw+Ar776is6dO8e0wJSglhsREZGEOaaWm0cffRSbzca//vUvZs+eTadOnQB44403uOiii2JaYEo4KNxU1anlRkREJJ6OqeWmS5cuvPrqq032P/LII8ddUEqqH1BsM8L4fN4kFyMiIpLajincAIRCIV5++WXWrVuHYRj06dOHyy+/HKvVGsv6UkN9yw1A0FeTxEJERERS3zGFm6+//pqLL76YrVu3ctJJJ2GaJl999RXFxcW89tpr9OjRI9Z1tm5WO6bFjhEOEPbVYJomhmEkuyoREZGUdExjbu644w569OhBeXk5K1euZNWqVZSVlVFSUsIdd9wR6xpTQ/2MKYfpwxsIJ7kYERGR1HVMLTcLFy5k2bJl5ObmRvfl5eXx29/+lhEjRsSsuJRiTwOfhzR8VPkCuB3qvhMREYmHY2q5cTqdVFVVNdlfXV2Nw+E47qJSkVE/qNil9aVERETi6pjCzaWXXsrNN9/MRx99hGmamKbJsmXLmDx5Mpdddlmsa0wNB60vpXvdiIiIxM8xhZs//elP9OjRg2HDhuFyuXC5XAwfPpyePXsyc+bMGJeYIqIrg3vVciMiIhJHxzTmJicnh3//+998/fXXrFu3DtM06du3Lz179ox1famjfkCxC60MLiIiEk9HHG6+bbXv999/P/r84YcfPuaCUpYjHYA0Q2NuRERE4umIw82qVauO6Dzdv+UwGhbPxEeVFs8UERGJmyMON++9914860h9WjxTREQkIY5pQLEcg4NmS2nMjYiISPwo3CTKQd1SGnMjIiISPwo3idIwoFjdUiIiInGlcJMoDVPBDb9abkREROJI4SZRojfx82nMjYiISBwp3CTKwbOl1HIjIiISNwo3ieJomC0VWRVcRERE4iPp4WbWrFmUlJTgcrkYPHgwixcvbvF8n8/H3XffTdeuXXE6nfTo0YOnn346QdUeB7XciIiIJMQxrS0VK/PmzWPq1KnMmjWLESNG8PjjjzNmzBjWrl1Lly5dmn3P1Vdfzc6dO3nqqafo2bMnFRUVBIOtICwcNOam2hfENE3dzVlERCQOkhpuHn74YW666SYmTZoEwMyZM3nrrbeYPXs2M2bMaHL+m2++ycKFC9mwYQO5ubkAdOvWrcXv8Pl8+Hy+6GuPxxO7H+BoHDRbKhAy8QXDuOzW5NQiIiKSwpLWLeX3+1mxYgWjR49utH/06NEsXbq02fe88sorDBkyhN/97nd06tSJ3r1789Of/pS6urrDfs+MGTPIzs6ObsXFxTH9OY7YQd1SgO51IyIiEidJa7nZvXs3oVCIwsLCRvsLCwvZsWNHs+/ZsGEDH3zwAS6Xi5deeondu3fz4x//mL179x523M306dMbrWju8XiSE3AcB7qlAKq9QfIznImvQ0REJMUltVsKmq4i3tJYlHA4jGEYzJ07l+zsbCDStfW9732Pxx57DLfb3eQ9TqcTp/MECBH1LTcuI4BBmG376+iWn57kokRERFJP0rql8vPzsVqtTVppKioqmrTmNOjYsSOdOnWKBhuAPn36YJomW7ZsiWu9x60+3AC48fPRxr1JLEZERCR1JS3cOBwOBg8ezIIFCxrtX7BgAcOHD2/2PSNGjGDbtm1UV1dH93311VdYLBY6d+4c13qPm80VferGx7INe5JYjIiISOpK6n1upk2bxpNPPsnTTz/NunXruOuuuygrK2Py5MlAZLzM+PHjo+dfd9115OXlceONN7J27VoWLVrEz372M374wx822yV1QrFYDgwqNnysKt+PNxBKclEiIiKpJ6ljbq655hr27NnDAw88wPbt2+nfvz+vv/46Xbt2BWD79u2UlZVFz8/IyGDBggXcfvvtDBkyhLy8PK6++moefPDBZP0IR8fuhkAtndNNtlSHWVW2n2E98pJdlYiISEoxTNM0k11EInk8HrKzs6msrCQrKyuxX/7IAKgs4w9dZ/PnL7O5c2Qv7rqwd2JrEBERaYWO5u930pdfaFPqb+Q3sDAye0vjbkRERGJP4SaR6sNNv4JIb6DG3YiIiMSewk0iOSL3tengDtM+04k/GKa0fH9yaxIREUkxCjeJVN9yYwTrOLN7ZCCxuqZERERiS+EmkRpu5OevUbgRERGJE4WbRGoIN4E6zuweWdV8ZZnG3YiIiMSSwk0iORrCTS0l+ekadyMiIhIHCjeJZD8QbgzDUNeUiIhIHCjcJNJB3VKAwo2IiEgcKNwkUv1sKfy1ABp3IyIiEgcKN4l0ULcUoHE3IiIicaBwk0iOxuFG425ERERiT+EmkQ5puQGNuxEREYk1hZtEit7E7+Bwo3E3IiIisaRwk0gNA4rrZ0uBxt2IiIjEmsJNItUvnEmgJrpL425ERERiS+EmkZppuQGNuxEREYklhZtEOuQmfg007kZERCR2FG4S6aBVwTHN6G6NuxEREYkdhZtEauiWMkMQCkR3a9yNiIhI7CjcJFLDgGJoNKgYNO5GREQkVhRuEslqB4st8lzjbkREROJC4SbR7PWtNwfdyA807kZERCRWFG4SzZkReaza3mi3xt2IiIjEhsJNonU7O/K47j9NDinciIiIHD+Fm0TrPy7yuOYlCDceWzNU425ERESOm8JNonU/D9ztoKYCNn3Q+FB+OgUadyMiInJcFG4SzeaAPpdFnn/+QqNDGncjIiJy/BRukqGha2rdKxD0NzrUMCVc4UZEROTYKNwkQ7ezIL091O2DDe83OtTQcqNxNyIiIsdG4SYZLFbo993I80O6pg4ed7OqbH/iaxMREWnlFG6SpaFr6ovXGt2t2DAMzu6VD8A/Py5LRmUiIiKtmsJNsnQ+HbKLwV8F6xc0OjTprO4AvPrZNr6uqEpGdSIiIq2Wwk2yWCyH7ZrqW5TF6L6FmCb8+d2vk1CciIhI66Vwk0wNXVNfvQW+xi00d4zsBcB/Pt3GN7uqE12ZiIhIq6Vwk0wdB0JuDwjWwZdvNjrUv1M2o/oUEjbhUbXeiIiIHDGFm2QyjAOtN4d0TQHcWd968+/SrWxQ642IiMgRUbhJtoZw8/U7kfveHGRA52xGntw+0nrznlpvREREjoTCTbK1Pxna94NwANa92uTwnaMaWm+2sWl3TaKrExERaXUUbk4E/a+MPDbTNXVK5xzOP6mAUNhU642IiMgRULg5ETSEm40LoXpXk8N3juoNwEurtrJ5j1pvREREWqJwcyLI7Q5Fp4EZhrUvNzl8anEO5/aOtN48ptYbERGRFincnCiis6ZebPZww9ibF1dupXxvbaKqEhERaXUUbk4UDXcrLlsKlVubHD6tSzvO7pVPUK03IiIiLVK4OVFkd4IuwyPP17zU7ClT61tv/rVii1pvREREDiPp4WbWrFmUlJTgcrkYPHgwixcvPqL3LVmyBJvNxqmnnhrfAhOphVlTAIO75nJWz0jrzaz3v0lgYSIiIq1HUsPNvHnzmDp1KnfffTerVq3i7LPPZsyYMZSVlbX4vsrKSsaPH8/IkSMTVGmC9L0CDAtsWwl7NzR7yp3R1ptytu6vS2BxIiIirUNSw83DDz/MTTfdxKRJk+jTpw8zZ86kuLiY2bNnt/i+W265heuuu45hw4YlqNIEySiAknMjzw8zsPj0brkM75FHIGQyS2NvREREmkhauPH7/axYsYLRo0c32j969GiWLl162PfNmTOHb775hnvvvfeIvsfn8+HxeBptJ7RvmTUFB9ac+t/l5WxT642IiEgjSQs3u3fvJhQKUVhY2Gh/YWEhO3bsaPY969ev55e//CVz587FZrMd0ffMmDGD7Ozs6FZcXHzctcdVn0vBYoeKNbBzbbOnDO2ex5ndcwmETH7z2jpM00xwkSIiIieupA8oNgyj0WvTNJvsAwiFQlx33XXcf//99O7d+4g/f/r06VRWVka38vLy4645rtztoFd9a9YbP4dwqNnTpo/pg81i8Nrq7Ty7dFPi6hMRETnBJS3c5OfnY7Vam7TSVFRUNGnNAaiqqmL58uXcdttt2Gw2bDYbDzzwAJ9++ik2m41333232e9xOp1kZWU12k54Fz4A9jTYtBiW/qnZUwYW5/Cri/sA8JvX17GybF+z54mIiLQ1SQs3DoeDwYMHs2DBgkb7FyxYwPDhw5ucn5WVxerVqyktLY1ukydP5qSTTqK0tJShQ4cmqvT4y+8JY/478vzdB2HrymZPu3FENy4Z0JFAyOS2uSvZW+NPYJEiIiInpqR2S02bNo0nn3ySp59+mnXr1nHXXXdRVlbG5MmTgUiX0vjx4yOFWiz079+/0da+fXtcLhf9+/cnPT09mT9K7A26AfpcBuEgvDAJfNVNTjEMg9+OG0D3/HS2VXq58/lVhMIafyMiIm1bUsPNNddcw8yZM3nggQc49dRTWbRoEa+//jpdu3YFYPv27d96z5uUZRgw9o+Q1Qn2fgNv/rLZ0zJddmb/YDAuu4XF63fz53fXJ7hQERGRE4thtrGpNh6Ph+zsbCorK1vH+JuNi+HZsYAJV/8N+l7e7GkvrtzCtP/9FMOAZ288g3N6FyS2ThERkTg6mr/fSZ8tJd+i5Gw4a2rk+St3QOWWZk+78rTOXDe0C6YJdz6/Sve/ERGRNkvhpjU471dQNAi8++GlyYedHn7PpX3p3ymLfbUBpvxzJf5gOLF1ioiInAAUbloDmwPGPQX29Mj08CV/bPY0l93K7OsHk+WysapsPw+9vi7BhYqIiCSfwk1rkdfjwPTw934DW1c0e1pxbhqPXHMqAM8s3cSrn21LUIEiIiInBoWb1mTQDyIDisNBeOFHzU4PBxjZp5Afn9cDgF/86zO+rmj+PBERkVSkcNOaNJke/ovDnjrtwt4M655HjT/ExDkfU7anNoGFioiIJI/CTWvjbgdXPgEYsOof8Om8Zk+zWS386dpBdMtLY8u+Oq56fCnrd1YltlYREZEkULhpjbqdBWdPizx/+VZY/a9mTyvIdPK/twzjpMJMdnp8XPPEMj7fWpnAQkVERBJP4aa1Ov9uOPV6MEPw4o/gs/nNntY+y8XzN5/JKZ2z2Vvj59onlrF8094EFysiIpI4CjetlcUKlz0aGWRshuGlmw/bRdUu3cHcSUM5oySXKl+QG576mMXrdyW4YBERkcRQuGnNLBYY+2c4bXx9wLkFSp9r9tRMl51nbzyDc3sXUBcIcdMzy3lrzY4EFywiIhJ/CjetncUCl/4RBk8EzMgYnFVzmz3V7bDy1/FDGNO/A/5QmB/PXclLq5pfzkFERKS1UrhJBRYLXPIIDLkJMOHfU2Dl35s91WGz8OdrBzHutM6EwibT/vdT/rFsc2LrFRERiSOFm1RhscAlf4DTfwSY8MptsOLZZk+1WS38z/dOYcKwrpgm/NfLn/PYe1/TxhaIFxGRFKVwk0oMAy7+Hzjjlsjr/9wBy+c0e6rFYnDfZf2idzL+n7e+5Ja/r6CyNpCoakVEROJC4SbVGEZkDaqht0ZevzoVlj4KzbTKGIbBzy86mQcu74fDauHttTu5+E+LWVW2L7E1i4iIxJDCTSoyDLhoBpw5JfL67bth3g+gtvn724wf1o0Xbh1O17w0tu6v46q/fMhfF21QN5WIiLRKCjepyjDgO7+Bi34LFjt88So8fg6ULWv29AGds/nP7WdxySkdCYZNfvP6OiY9u5x9Nf4EFy4iInJ8FG5SmWHAmbfCpAWQ2x0qy2HOxbDo9xAONTk9y2Xn0WsH8eAV/XHYLPzfFxVc8qfFrNisOxqLiEjroXDTFhQNglsWwYCrI8s1vPv/4O/fhaqmN/EzDIMfnNmVl348nJL8dLZVern68WXMfv8bwmF1U4mIyIlP4aatcGZGVhO/YjbY02DjQpg9Ata/0+zp/Yoi3VSXn1pEKGzy329+wcRnPqF8b22CCxcRETk6htnGRo16PB6ys7OprKwkKysr2eUkx66v4F83ws7PI69H3AkX/Bqs9ianmqbJvE/KufeVNfiCYVx2C1PO68mPzumOy25NcOEiItJWHc3fb4Wbtirghbf/Cz75a+R1x1Ph0oeh0+BmT/+6oor/evlzlm2IjL/plpfGvZf14/yT2ieoYBERacsUblqgcHOIdf+JLNfgrQQMGDwBRt4LablNTjVNk1c+3cZvXltHRZUPgAv7FnLPpX0pzk1LcOEiItKWKNy0QOGmGVU7YcE98NnzkdfudjDqPhg0PrKswyGqfUH+9H/refqDjQTDJk6bhSnn9+RmdVWJiEicKNy0QOGmBZuWwOs/g4o1kddFp8Elvz9sV9X6nVXc8+81fLhhDwBd89K4b2w/zj9ZXVUiIhJbCjctULj5FqFgZBzOew+Bz8ORdFX957Pt/Oa1tez0RLqqhpbkcteFvTmze16CixcRkVSlcNMChZsj1FxX1QW/hkE/AJuzyekNXVXPLNmEPxQGYFj3PKaO6sVQhRwRETlOCjctULg5SpuXwms/PdBVldEBht4CQ26MBJ5DbNtfx6z3v2beJ+UEQpFfreE98rjrwt6c3q1py4+IiMiRULhpgcLNMQgFYflT8MFMqNoW2WdPh9NuiCzv0K5bk7ds3V/HrPe+5n+XHwg5Z/XMZ+qoXgxRyBERkaOkcNMChZvjEPTDmhdh6Z8P3ADQsECfy2D4HdC56cDjLftqeey9b5i/vJxg/fINZ/fK59ZzezCsRx6GYSTyJxARkVZK4aYFCjcxYJqw4f1IyPnm/w7s7zIcht8GvS8CS+Mp4eV7a3nsva/514ot0ZDTuzCDicNL+O6gTrgdmkIuIiKHp3DTAoWbGNvxOXz4GKyeD+FAZF+7bnDGzXDq9eDOaXR6+d5anli0gRdWbqHWH1mZPNtt5/unF/ODM7vqZoAiItIshZsWKNzEiWc7fPQXWDGn/m7HRMblnHotnHELFPRudHplXYD5y8v524ebKatfjNNiRO54PHF4CWd2z1WXlYiIRCnctEDhJs78NfDZ/8JHj8OudQf297gAhk6Gnhc2uutxKGzy3hcVPLN0Ex98vTu6/+QOmVx/ZlcuO6WI7LSmC3qKiEjbonDTAoWbBDFN2LgoEnK+fB2o/zXL7R7psjrlmiY3BfxqZxXPLt3Eiyu3UheIdFk5bBa+068DVw3uzIie+Vgtas0REWmLFG5aoHCTBHs3widPwsq/g6++y8rqiAw8Hngt9BwFNkf09MraAPNXlDN/+Ra+3FkV3d8x28W40zozbnBnSvLTE/1TiIhIEinctEDhJol81fDZPFj+9IGp5ADuXBjwPRj4/ch6VvVjbUzT5POtHuavKOffpduorAtE33J6t3ZcNbiYi0/pSIbTluifREREEkzhpgUKNyeIHavh0+cjs6yqdx7Yn987EnIGXA05xdHd3kCId9btZP7yLSxev4v62eQ4bRbO7V3AJad0ZGSfQgUdEZEUpXDTAoWbE0woGLlnzqfPwRevQbCu/oABXc6M3CCwz9hGQWdHpZcXV23hX8u3sGF3TXS/oz7oXDygA6P6FJLp0kBkEZFUoXDTAoWbE5jXA+teibTobFrc+FjRadD3skjYyesBRLqt1m2v4vXV23l99fbGQcdq4Zze+Vw8INKik+1W0BERac0UblqgcNNKVG6Bdf+Bta9A2YdEZ1sBFPaPhJy+l0HByWAYmKbJFzuqeGP1dl5bvZ1vdh0IOjaLwendchnZpz3nn9ye7vnpuoeOiEgro3DTAoWbVqhqJ3zxaqRVZ+NiMEMHjuV2j9w7p9eF0HUEONIwTZOvdlZHW3TWV1Q3+riueWmcf1J7Lji5PUO75+K0aekHEZETncJNCxRuWrnavZH75qx9BTa8ByH/gWM2VyTg9BwVCTt5PcEw2LS7hne/qOC9Lyv4aMNe/KFw9C1pDisjeuZzwcntObtXPp3bafkHEZETkcJNCxRuUojXAxsXwtfvwPp3wLOl8fGcLpGg02MkdB0OabnU+IJ88PVu3vuigne/qKCiytfoLSX56ZzVM5+ze+VzZo88sjQoWUTkhNCqws2sWbP4n//5H7Zv306/fv2YOXMmZ599drPnvvjii8yePZvS0lJ8Ph/9+vXjvvvu4zvf+c4Rf5/CTYoyTdj1JXy9IBJ2Ni9t3KoD0L5vJOR0HQ5dhmNmdmDNNg/vfVHB+1/torR8P6Hwgf85WC0GpxbnRMPOwOIc7FYLIiKSeK0m3MybN48bbriBWbNmMWLECB5//HGefPJJ1q5dS5cuXZqcP3XqVIqKijj//PPJyclhzpw5/P73v+ejjz5i0KBBR/SdCjdthL8mMj7n6wWRx91fNj0nt3t92BkBXYbhcXfiww17+WD9bj74ejcbD5p9BZDhtHF6t3acUZLH0O65DOiUrbAjIpIgrSbcDB06lNNOO43Zs2dH9/Xp04crrriCGTNmHNFn9OvXj2uuuYZ77rnniM5XuGmjqndFZl1tXgqbl0RuIsghv/oZhdD5dCgeCsVD2eLuxQcbq1n89W6WfL2b/bWBRqe77VZO65rD0JI8zijJ5dTiHFx2DU4WEYmHo/n7nbTbufr9flasWMEvf/nLRvtHjx7N0qVLj+gzwuEwVVVV5ObmHvYcn8+Hz3dgXIXH4zm2gqV1yyiITB3ve1nkdd1+KP84EnQ2L4VtKyN3Sv7i1cgGdLY6+H7HU/l+8RmEBp7Oekdfluy08/HGPXy8cS/7agMs+XoPS77eA0TurXNqcQ6ndW3HoC45DCrOoX2WK0k/sIhI25W0cLN7925CoRCFhYWN9hcWFrJjx44j+ow//OEP1NTUcPXVVx/2nBkzZnD//fcfV62Sgtw50Ht0ZAMI1MG2VZHAU/4xlH8Etbthy8ew5WOswMnAyVmduanTaYQvGMLW9D58UFPM0nIvH23YQ0WVj4837eXjTXujX1OU7WJQl3acWpzDqV1y6F+Ujduh1h0RkXhK+kI8h95MzTTNI7rB2nPPPcd9993Hv//9b9q3b3/Y86ZPn860adOirz0eD8XFxYc9X9oou/vAYGOIDFDeuyESdLbUB56dayIzsjxbsKx7hWLgWsPCtQUnY/Y7jT05A1gZ7M7CynxWlFfz1c4qtlV62VZ/Y0GIDFLu0zGTgZ1zOKVzNv07ZdO7MFNjd0REYihp4SY/Px+r1dqklaaioqJJa86h5s2bx0033cT8+fMZNWpUi+c6nU6cTudx1yttjGFElnnI6wGnXhvZ56uCbaWwdTlsXQFbV4JnK1SsxahYSz4wGhhtdUDByQSG9GOLswefBbrw3v72LNkWYleVj8+3evh8q4e5H0U+1mGz0LdjFgM6ZTOgczYDOmXTq30GNgUeEZFjkvQBxYMHD2bWrFnRfX379uXyyy8/7IDi5557jh/+8Ic899xzXHHFFUf9nRpQLDHl2R4Zr7N1Rf22CnyVzZ5qZnfGm9eXLY6erA525kNPPu9UZLDP2/Rcl91Cn45Z9O2YRd+iyOPJHbLUpSUibVarmS3VMBX8L3/5C8OGDeOJJ57gr3/9K2vWrKFr165Mnz6drVu38re//Q2IBJvx48fzxz/+kSuvvDL6OW63m+zs7CP6ToUbiSvThP2bI7Oxdnweedy5GvaXNX+6xUYgu4Td7hI20JkVdYUs3JvH574C/DS+gaDFiNxksG9RdjT09OmYSUGGU2tliUjKazXhBiI38fvd737H9u3b6d+/P4888gjnnHMOABMnTmTTpk28//77AJx33nksXLiwyWdMmDCBZ5555oi+T+FGkqJuf2TMzs7PYcdnUPFF5KaD/qpmTzcNCzVpxWx3dGF9sAMravP5tLaAb8wi9tH49zbLZaNn+wx6FGQ0euzczq2uLRFJGa0q3CSawo2cMEwzMmZnV33QaXis+OKwXVsAXls222zFfBks5DNvARvCHdhsdmCTWYiXA+PLHFYLJfnp9GifTs+CDHrUB5+S/HTSnUmfSyAiclQUblqgcCMnPNOEqh2RuyrvXh/Z9tQ/Vpa3+Nb9tnzK6cCX/ny+CUUCz2azkHKzPVUcWBS0KNsVDTs9CtLpUZBB94IMCrPUxSUiJyaFmxYo3Eir5q+BPd8cCDu710emrO/9BryHb+0BqDYyKDfbsymUR7nZni1mfv1jAVvMfOpwke6wUlKQTkl+Bt3z0+lekE73/AxKCtLJUGuPiCSRwk0LFG4kZdXuhb0bI0Fn74YD255voG7vt759j5nJFrOArWY+W838Js/dme3olpdOcW4axbluitul0SUvjeJ2abTPdGKxqMVHROJH4aYFCjfSJvmqIjO29pfBvs31zzdHtn1lLY7xaeAx09hq5rHDzGW7mcsOM4/t5LLTbMduSz7W7E7k5uZTnJdGp5w0Ordz06mdm87t3JrRJSLHTeGmBQo3Is2o2wf7yyNjevaX1T+vf9xfdkQtPwDVpisafrbXh5/tZh67LPmYGUXYcovJy8unU46bzu3S6NTOTaccN4VZLqxq+RGRFijctEDhRuQY+Kqhckv98hPbGm2mZyvhym1YffuP6KOqTDc7zFx2mO3YSeRxF7n40woxsopwtOtMdkERndpl0CHbRWGWi8IsJ9luu1p/RNowhZsWKNyIxIm/Fqq214egbdEgFN6/heC+LRhV27D79x/RRwVNCxXksMfMYq+ZxR6yqDSy8DtzCbvyMDILcGS2x53Tnqz8TuTl5tKxvgXIZdddnEVS0dH8/db0BxGJDUfagfW4DmIBHA0v/DWRJSs8WyKPVdsIe7bj27uFUOVWrNU7cHp3YzPCFLGXIuOQ7rBA/XbIvQ9rTSe7zGw+J4dKSztqnfkEXPmQUYgtqwPudoVktCsgu10HcvPyyctya7FSkRSmcCMiieNIh/yeka2eBXAffE4oCDUVkVagmt1Qs5tAVQV1+3fi91QQrt6NUbsbu28vbv8+nKaXNMNHV6OCrlREPsNXv1UCWxuXEDYNPKThMTKptWbhtWcTdORgutthpOdhzyzAmV1IRm4h2XlFZOQWYrhzwaIwJNJaKNyIyInFaoOsoshWz16/NctXDTUVmFU7qd27nao9W/Du207IsxOjeid2725c/v2khytJM+uwGCY51JBDDYR2QAjwAp7DlxTCQrWRQY0tB68jl6ArDzMtDyO9AHtWAe6cQjJyO5KWU4glowBcWWBzHv4DRSSuFG5EpHVzZoAzAyO3O+ldIb2lc0MBwjV78eyrwLO3gur9u6ir3IW/ek+kRahub6RFKLCPjFAl7cxKso1arITJNj1kBzwQKIMaYE/LZQWw47OmEbCmE7JnYDozMJyZWN3Z2NMycWREWomM9AJIz4e0vPrH/EgXn4gcM4UbEWk7rHYsWYXkZBWS0/XbT/cGQpTvr2bv7h1U7d1O3b6d+D27CNfsxlK7G7t3D07/PjJC+8kOV5JneGhnVANgJ4A9VAmhSvATCURHyG9x47W3I+DMBmcmFlcWNncWjvQcHGnZGK4scGaCs/7RlX3QlgWOTHWjSZumcCMichguu5XigmyKC7KBk1o81xcMsbfGz+rKOior9+LZv5eaqv3UVe3HW1OJv7aSUJ0H0+vB8FeTZXrINarIxUOeUUWu4SEXDw4jhCNch8NXB75tx1S3iUHIkUnYEQk8Fnc21rQcDHfOQSEo55BQlB1pBXNkRsZG2ZygqffSSinciIjEgNNmpWO2m47ZbiC3xXNN06QuEGJ/baB+87OpLsD+Gj+11XsJenYRrN5FqHY/wdpKTK8HfFXYQzVkUkcGdWQYkccsI7Ivy6ghi1qcRhADE5vfA34PVLe82Opha7TYMO3p4EjHcGZiONLruwCzIpvroEdX9kH7sutblTIiIcmRARZNz5fEUrgREUkwwzBIc9hIc9goynEfcvTw/WUNrUN7qv3sqfGzu9rHVzV+PN4gnroAnlo/tbU1hOr2E66rBG8lFp8Hd6iKTKOWLGqjIejAYy1Z1JBl1JCOjzTDF6kxHMTwVUaW5qg6bElHxLS5D4QjR8OWfiD8ONIj44wO3W9PA7u7mcf6zeZS65I0S+FGRKSVaNw6dOR8wRCVtQH21wXYV+NnX31r0da6APtq/eyviTxWeYPUeL0EvDWEvVWYvmqc4TrSDS/p1JGOl0yjjkxqo2Ep06glk7pGrzPqz7UZYQCMYB0E66B2d+wvir0+FDkzD9qyDnmdWR+c0iLByZ5++OdWuwJTClC4ERFJcU6blfZZVtpnuY7qfaZp4guG8XgDVHmD9VuAyroAnrogHm+A7XUBPN4DryPHAlTWBvB6a7GHauvDUf1meEnDSwZe3IaPdLyk1R9PI3LPonR8pNWf68KPCz9uw4cbP27Dj53ggSIDtZGtpiI2F8uwHmghcqQ133oUbV1qCEeHbA3n2pyR1qXo5jzwqAAVVwo3IiLSLMMwcNmtuOxW2mce/ftN08QbCFNZF2B/nZ/K2kj4qawL4PEGqfEFqfYF2eGNPFZ7A5FHX4hqXyRQVXuDBMONVwmyEooEHvy4DS/p+MiglgyjLjImyTgwLimTSEtTmuElrb7bLR0f6RZf5DVeXPgOBCYzBP6qyHYUM9yO2qFhp9Fj/XOr85CQlVb/PL1+f3NB6pBHq6NNBiqFGxERiQvDMHA7rLgdVjpkH12rUYOG1qNI+ImEoKqGMOQLUO0NUlV/rGH/Lm+QDdGgFNlf4wviC4YP+z12grjxNWolSsOLy/DjpqHVqCEQ+aKhqqG1KdPiI9OoD0+GFycBnARw4MduBrCbPiwcFNKC3shG5TFdl2Nic4PdFQlEtvpHu+uQ5wcFJfuhLU4HvW4Y8xQNVu4D7z/4MUkUbkRE5IR1cOtRfsbx3fU5GApTGwhR6wtR6w9S6w9R44s8VvuC1PrrW4289cHJF4q2KO08qEWpzh+i1h+iLhDiyJeeNrETwokfJwFc+HEaDQEo8tjwOrL5cRkBMq0BsqwBMi1+Miw+0i1+0usDV5rhw4UPp+nDTgCH6cdm+rGF/VjDfmxh3yEXoH7sU92+47qORyQtH37+Tfy/5zAUbkREpE2wWS1kWS1kuQ67mMdRaeh2awhKke0wz31Bavwh6vyRx+gxX4gafxDPQUGrxh88EJpCx1VhNFC5COAy/DjxH9RC5SfNCJBlC5BpDZJpDZBuDZJmCZJmCZBmCeE2/LiNA8HLhR+H6cdu+rGbPmzhyGYN+bCEfFhCXgwzRMjqJJk3AFC4EREROQYHd7vlxfBzG0JTtS+INxBpIaqrbymqC4TwHvS8LhqkIsGp1h+iNro/2Oi4NxhiX/17wyYQkwDVlI0gHSwGH8T2Y4+yBhERETlhHBya4sE0TfyhMN5AOBKeDglLddGAFIx2vx0cknyBA91ydYFQo8+IPLdicSV34ViFGxERkTbEMAycNitOm5Vsd2y66A5lHvlgpLjQymoiIiISU0aSp50r3IiIiEhKUbgRERGRlKJwIyIiIilF4UZERERSisKNiIiIpBSFGxEREUkpCjciIiKSUhRuREREJKUo3IiIiEhKUbgRERGRlKJwIyIiIilF4UZERERSisKNiIiIpBRbsgtItIZl2D0eT5IrERERkSPV8He74e94S9pcuKmqqgKguLg4yZWIiIjI0aqqqiI7O7vFcwzzSCJQCgmHw2zbto3MzEwMw4jpZ3s8HoqLiykvLycrKyumny1N6Xonlq53Yul6J5aud2Idy/U2TZOqqiqKioqwWFoeVdPmWm4sFgudO3eO63dkZWXpfxwJpOudWLreiaXrnVi63ol1tNf721psGmhAsYiIiKQUhRsRERFJKQo3MeR0Orn33ntxOp3JLqVN0PVOLF3vxNL1Tixd78SK9/VucwOKRUREJLWp5UZERERSisKNiIiIpBSFGxEREUkpCjciIiKSUhRuYmTWrFmUlJTgcrkYPHgwixcvTnZJKWPRokWMHTuWoqIiDMPg5ZdfbnTcNE3uu+8+ioqKcLvdnHfeeaxZsyY5xbZyM2bM4PTTTyczM5P27dtzxRVX8OWXXzY6R9c7dmbPns0pp5wSvZHZsGHDeOONN6LHda3ja8aMGRiGwdSpU6P7dM1j57777sMwjEZbhw4dosfjea0VbmJg3rx5TJ06lbvvvptVq1Zx9tlnM2bMGMrKypJdWkqoqalh4MCBPProo80e/93vfsfDDz/Mo48+yieffEKHDh248MILo+uIyZFbuHAhU6ZMYdmyZSxYsIBgMMjo0aOpqamJnqPrHTudO3fmt7/9LcuXL2f58uVccMEFXH755dH/wOtax88nn3zCE088wSmnnNJov655bPXr14/t27dHt9WrV0ePxfVam3LczjjjDHPy5MmN9p188snmL3/5yyRVlLoA86WXXoq+DofDZocOHczf/va30X1er9fMzs42//KXvyShwtRSUVFhAubChQtN09T1ToR27dqZTz75pK51HFVVVZm9evUyFyxYYJ577rnmnXfeaZqmfr9j7d577zUHDhzY7LF4X2u13Bwnv9/PihUrGD16dKP9o0ePZunSpUmqqu3YuHEjO3bsaHT9nU4n5557rq5/DFRWVgKQm5sL6HrHUygU4vnnn6empoZhw4bpWsfRlClTuOSSSxg1alSj/brmsbd+/XqKioooKSnh+9//Phs2bADif63b3MKZsbZ7925CoRCFhYWN9hcWFrJjx44kVdV2NFzj5q7/5s2bk1FSyjBNk2nTpnHWWWfRv39/QNc7HlavXs2wYcPwer1kZGTw0ksv0bdv3+h/4HWtY+v5559n5cqVfPLJJ02O6fc7toYOHcrf/vY3evfuzc6dO3nwwQcZPnw4a9asifu1VriJEcMwGr02TbPJPokfXf/Yu+222/jss8/44IMPmhzT9Y6dk046idLSUvbv388LL7zAhAkTWLhwYfS4rnXslJeXc+edd/L222/jcrkOe56ueWyMGTMm+nzAgAEMGzaMHj168Oyzz3LmmWcC8bvW6pY6Tvn5+Vit1iatNBUVFU0SqcRew8h7Xf/Yuv3223nllVd477336Ny5c3S/rnfsORwOevbsyZAhQ5gxYwYDBw7kj3/8o651HKxYsYKKigoGDx6MzWbDZrOxcOFC/vSnP2Gz2aLXVdc8PtLT0xkwYADr16+P+++3ws1xcjgcDB48mAULFjTav2DBAoYPH56kqtqOkpISOnTo0Oj6+/1+Fi5cqOt/DEzT5LbbbuPFF1/k3XffpaSkpNFxXe/4M00Tn8+nax0HI0eOZPXq1ZSWlka3IUOGcP3111NaWkr37t11zePI5/Oxbt06OnbsGP/f7+Mekizm888/b9rtdvOpp54y165da06dOtVMT083N23alOzSUkJVVZW5atUqc9WqVSZgPvzww+aqVavMzZs3m6Zpmr/97W/N7Oxs88UXXzRXr15tXnvttWbHjh1Nj8eT5Mpbn1tvvdXMzs4233//fXP79u3Rrba2NnqOrnfsTJ8+3Vy0aJG5ceNG87PPPjN/9atfmRaLxXz77bdN09S1ToSDZ0uZpq55LP3kJz8x33//fXPDhg3msmXLzEsvvdTMzMyM/m2M57VWuImRxx57zOzatavpcDjM0047LTp1Vo7fe++9ZwJNtgkTJpimGZlSeO+995odOnQwnU6nec4555irV69ObtGtVHPXGTDnzJkTPUfXO3Z++MMfRv+7UVBQYI4cOTIabExT1zoRDg03uuaxc80115gdO3Y07Xa7WVRUZF555ZXmmjVrosfjea0N0zTN42//ERERETkxaMyNiIiIpBSFGxEREUkpCjciIiKSUhRuREREJKUo3IiIiEhKUbgRERGRlKJwIyIiIilF4UZERERSisKNiLRJhmHw8ssvJ7sMEYkDhRsRSbiJEydiGEaT7aKLLkp2aSKSAmzJLkBE2qaLLrqIOXPmNNrndDqTVI2IpBK13IhIUjidTjp06NBoa9euHRDpMpo9ezZjxozB7XZTUlLC/PnzG71/9erVXHDBBbjdbvLy8rj55puprq5udM7TTz9Nv379cDqddOzYkdtuu63R8d27d/Pd736XtLQ0evXqxSuvvBI9tm/fPq6//noKCgpwu9306tWrSRgTkROTwo2InJB+/etfM27cOD799FN+8IMfcO2117Ju3ToAamtrueiii2jXrh2ffPIJ8+fP55133mkUXmbPns2UKVO4+eabWb16Na+88go9e/Zs9B33338/V199NZ999hkXX3wx119/PXv37o1+/9q1a3njjTdYt24ds2fPJj8/P3EXQESOXUzWFhcROQoTJkwwrVarmZ6e3mh74IEHTNM0TcCcPHlyo/cMHTrUvPXWW03TNM0nnnjCbNeunVldXR09/tprr5kWi8XcsWOHaZqmWVRUZN59992HrQEw/+u//iv6urq62jQMw3zjjTdM0zTNsWPHmjfeeGNsfmARSSiNuRGRpDj//POZPXt2o325ubnR58OGDWt0bNiwYZSWlgKwbt06Bg4cSHp6evT4iBEjCIfDfPnllxiGwbZt2xg5cmSLNZxyyinR5+np6WRmZlJRUQHArbfeyrhx41i5ciWjR4/miiuuYPjw4cf0s4pIYinciEhSpKenN+km+jaGYQBgmmb0eXPnuN3uI/o8u93e5L3hcBiAMWPGsHnzZl577TXeeecdRo4cyZQpU/j9739/VDWLSOJpzI2InJCWLVvW5PXJJ58MQN++fSktLaWmpiZ6fMmSJVgsFnr37k1mZibdunXj//7v/46rhoKCAiZOnMg//vEPZs6cyRNPPHFcnyciiaGWGxFJCp/Px44dOxrts9ls0UG78+fPZ8iQIZx11lnMnTuXjz/+mKeeegqA66+/nnvvvZcJEyZw3333sWvXLm6//XZuuOEGCgsLAbjvvvuYPHky7du3Z8yYMVRVVbFkyRJuv/32I6rvnnvuYfDgwfTr1w+fz8err75Knz59YngFRCReFG5EJCnefPNNOnbs2GjfSSedxBdffAFEZjI9//zz/PjHP6ZDhw7MnTuXvn37ApCWlsZbb73FnXfeyemnn05aWhrjxo3j4Ycfjn7WhAkT8Hq9PPLII/z0pz8lPz+f733ve0dcn8PhYPr06WzatAm3283ZZ5/N888/H4OfXETizTBN00x2ESIiBzMMg5deeokrrrgi2aWISCukMTciIiKSUhRuREREJKVozI2InHDUWy4ix0MtNyIiIpJSFG5EREQkpSjciIiISEpRuBEREZGUonAjIiIiKUXhRkRERFKKwo2IiIikFIUbERERSSn/H/TOk+wZcqBlAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot losses \n", + "losses = history.history['loss']\n", + "val_losses = history.history['val_loss']\n", + "\n", + "plt.plot(losses)\n", + "plt.plot(val_losses)\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('loss')\n", + "\n", + "plt.legend(['loss','val_loss'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0e498679-4267-4a12-8a26-28c2fd4119ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0940\n", + "test loss 0.09398777782917023\n", + "\u001b[1m313/313\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Evaluation on test data \n", + "test_loss = mymodel.evaluate(X_test,y_test)\n", + "print(\"test loss\",test_loss)\n", + "\n", + "test_labels_p = mymodel.predict(X_test)\n", + "test_labels_p = np.argmax(test_labels_p, axis=1 )\n", + "\n", + "n = 0\n", + "\n", + "f,axs = plt.subplots(1,10,figsize = (15,15))\n", + "for i in range(len(test_labels)): \n", + " if n >= 10 :\n", + " break\n", + " if (test_labels_p[i] != test_labels[i]):\n", + " axs[n].imshow(test_images[i],cmap='gray')\n", + " axs[n].set_title(f'{test_labels[i]} -> {test_labels_p[i]}')\n", + " axs[n].axis('off')\n", + " n= n+1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5c46a75-ba84-42ee-8824-74fbd79ef95a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/cnn-batchnorm-comparison/conv-pred.ipynb b/cnn-batchnorm-comparison/conv-pred.ipynb new file mode 100644 index 0000000..1705c3e --- /dev/null +++ b/cnn-batchnorm-comparison/conv-pred.ipynb @@ -0,0 +1,431 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7a66657b-f3e8-430a-8551-54536858cda2", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import datasets,utils,models,layers,optimizers,losses\n", + "import numpy as np \n", + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "markdown", + "id": "c7b33119-4f56-40c2-a9ac-e021855d5db1", + "metadata": {}, + "source": [ + "# data Prepration " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d5e08183-17c7-422f-b2ad-18f9659aef78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "(train_images , train_labels), (test_images,test_labels ) = datasets.mnist.load_data()\n", + "\n", + "# preprocessing\n", + "X_train = train_images.reshape(60000,28,28,1)\n", + "X_test = test_images.reshape(10000,28,28,1)\n", + "\n", + "# Normalizing (0,1 )\n", + "X_train = X_train.astype('float') / 255\n", + "X_test = X_test.astype('float') / 255\n", + "\n", + "y_train = utils.to_categorical(train_labels)\n", + "y_test = utils.to_categorical(test_labels) \n", + "\n", + "print(train_labels[0])\n", + "print(y_train[0])\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "23ff2c0e-ccbf-4a77-ba99-8a9a397a47f6", + "metadata": {}, + "source": [ + "# model creation " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "052f3a1e-1409-42ca-b99b-140ea2b9a4f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"functional\"\n",
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\n" + ], + "text/plain": [ + "\u001b[1mModel: \"functional\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                         โ”ƒ Output Shape                โ”ƒ         Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ input_layer (InputLayer)             โ”‚ (None, 28, 28, 1)           โ”‚               0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ conv2d (Conv2D)                      โ”‚ (None, 14, 14, 32)          โ”‚             320 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ conv2d_1 (Conv2D)                    โ”‚ (None, 7, 7, 64)            โ”‚          18,496 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ flatten (Flatten)                    โ”‚ (None, 3136)                โ”‚               0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense (Dense)                        โ”‚ (None, 10)                  โ”‚          31,370 โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
\n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ input_layer (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ conv2d (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m) โ”‚ \u001b[38;5;34m320\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m) โ”‚ \u001b[38;5;34m18,496\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ flatten (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3136\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m31,370\u001b[0m โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Please use WSL2 or the TensorFlow-DirectML plugin.\n" + ] + } + ], + "source": [ + "myInput = layers.Input(shape=(28,28,1))\n", + "conv1 = layers.Conv2D(32,3,activation='relu', padding='same', strides=2 )(myInput)\n", + "conv2 = layers.Conv2D(64,3, activation='relu', padding='same', strides=2 )(conv1)\n", + "\n", + "flat = layers.Flatten()(conv2)\n", + "out_layer = layers.Dense(10,activation='softmax')(flat)\n", + "\n", + "mymodel = models.Model(myInput,out_layer)\n", + "\n", + "\n", + "mymodel.summary()\n", + "mymodel.compile(\n", + " optimizer=optimizers.SGD(learning_rate=0.001),\n", + " loss=losses.categorical_crossentropy\n", + ")\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c2d83c86-ac20-4c4c-8b08-331a9fb383e8", + "metadata": {}, + "source": [ + "# Train and Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "80ed9096-af37-4844-9bf3-3d9f4106896a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", 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0.3168 - val_loss: 0.2965\n", + "Epoch 46/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - loss: 0.3151 - val_loss: 0.2947\n", + "Epoch 47/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - loss: 0.3135 - val_loss: 0.2935\n", + "Epoch 48/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - loss: 0.3118 - val_loss: 0.2921\n", + "Epoch 49/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - loss: 0.3103 - val_loss: 0.2907\n", + "Epoch 50/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - loss: 0.3086 - val_loss: 0.2898\n" + ] + } + ], + "source": [ + "history = mymodel.fit(X_train,y_train,batch_size=128,epochs=50,validation_split=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d67a7b08-2bed-45c7-968a-8eec643ec655", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot losses \n", + "losses = history.history['loss']\n", + "val_losses = history.history['val_loss']\n", + "\n", + "plt.plot(losses)\n", + "plt.plot(val_losses)\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('loss')\n", + "\n", + "plt.legend(['loss','val_loss'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0db70e62-8983-4bc6-bbe2-52d6fbbf68c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2894\n", + "test loss 0.28942954540252686\n", + "\u001b[1m313/313\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Evaluation on test data \n", + "test_loss = mymodel.evaluate(X_test,y_test)\n", + "print(\"test loss\",test_loss)\n", + "\n", + "test_labels_p = mymodel.predict(X_test)\n", + "test_labels_p = np.argmax(test_labels_p, axis=1 )\n", + "\n", + "n = 0\n", + "\n", + "f,axs = plt.subplots(1,10,figsize = (15,15))\n", + "for i in range(len(test_labels)): \n", + " if n >= 10 :\n", + " break\n", + " if (test_labels_p[i] != test_labels[i]):\n", + " axs[n].imshow(test_images[i],cmap='gray')\n", + " axs[n].set_title(f'{test_labels[i]} -> {test_labels_p[i]}')\n", + " axs[n].axis('off')\n", + " n= n+1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b9378ab-d1c6-4c6d-80e4-1fbdde35d385", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/Autoencoder-Denoising.md b/docs/Autoencoder-Denoising.md new file mode 100644 index 0000000..9b10906 --- /dev/null +++ b/docs/Autoencoder-Denoising.md @@ -0,0 +1,19 @@ +# Autoencoder Image Denoising โ€” MNIST + +## Setup + +A minimal fully-connected autoencoder: `Input(784) โ†’ Dense(32, relu) โ†’ Dense(784, sigmoid)`. Gaussian noise (factor 0.5) is added to MNIST digits, and the network is trained to reconstruct the *original clean* image from the noisy input โ€” binary crossentropy loss between reconstruction and ground truth. + +## Why a 32-unit bottleneck matters + +Compressing a 784-pixel image down to 32 values and then reconstructing it forces the network to learn which features actually matter for representing a digit, rather than memorizing pixels. If the reconstruction still looks like a recognizable digit after that bottleneck, it's evidence the encoder learned a genuinely useful compressed representation. + +## Result + +![Original, noisy, and denoised digits](images/autoencoder-denoising-result.png) + +Despite the aggressive compression (784 โ†’ 32 โ†’ 784), the reconstructions are clearly recognizable digits โ€” the noise is substantially removed and the core digit shape is preserved. + +## Notebook + +[`encoder-noisy-image.ipynb`](../autoencoder-denoising/encoder-noisy-image.ipynb) diff --git a/docs/CNN-BatchNorm.md b/docs/CNN-BatchNorm.md new file mode 100644 index 0000000..21d18ab --- /dev/null +++ b/docs/CNN-BatchNorm.md @@ -0,0 +1,31 @@ +# CNN BatchNorm Comparison โ€” MNIST + +## Setup + +A baseline CNN (`Conv2D(32) โ†’ Conv2D(64) โ†’ Flatten โ†’ Dense(10, softmax)`) compared against the identical architecture with BatchNormalization inserted after each conv layer. Both models use the same optimizer and learning rate (SGD, lr=0.001) โ€” BatchNorm is the only variable that changed, making this a clean ablation. + +## Training curves + +**Baseline CNN:** + +![Baseline CNN loss curve](images/cnn-baseline-loss.png) + +**CNN + BatchNorm:** + +![BatchNorm CNN loss curve](images/cnn-bn-loss.png) + +The BatchNorm version's curve drops faster and settles lower โ€” visible directly in the training dynamics, not just the final number. + +## Results + +| Model | Test loss | +|---|---| +| Baseline CNN | 0.289 | +| CNN + BatchNorm | 0.094 | + +Since the optimizer and learning rate were held constant, this ~3x reduction in test loss is attributable to BatchNormalization itself โ€” consistent with its known role in stabilizing gradients and accelerating convergence. + +## Notebooks + +- [`conv-pred.ipynb`](../cnn-batchnorm-comparison/conv-pred.ipynb) +- [`conv-pred-BN.ipynb`](../cnn-batchnorm-comparison/conv-pred-BN.ipynb) diff --git a/docs/Home.md b/docs/Home.md new file mode 100644 index 0000000..8b0fa6e --- /dev/null +++ b/docs/Home.md @@ -0,0 +1,9 @@ +# Deep Learning Portfolio โ€” Documentation + +Deep dives into each experiment: architecture, training configuration, and results โ€” illustrated with real plots from the notebooks. + +## Projects + +- [MLP Regularization](MLP-Regularization.md) โ€” Dropout comparison (with an honest note on a confounded variable) +- [CNN BatchNorm Comparison](CNN-BatchNorm.md) โ€” clean ablation, BatchNorm's effect isolated +- [Autoencoder Denoising](Autoencoder-Denoising.md) โ€” noisy-to-clean image reconstruction through a compressed bottleneck diff --git a/docs/MLP-Regularization.md b/docs/MLP-Regularization.md new file mode 100644 index 0000000..7f8a81a --- /dev/null +++ b/docs/MLP-Regularization.md @@ -0,0 +1,41 @@ +# MLP Regularization โ€” MNIST + +## Setup + +A baseline multilayer perceptron (`Dense(500) โ†’ Dense(100) โ†’ Dense(10)`) compared against the same architecture with `Dropout(0.2)` added after each hidden layer. + +## โš ๏ธ Two variables changed, not one + +Before looking at the results: the baseline uses SGD at `lr=0.001`, while the dropout run uses SGD at `lr=0.01` โ€” a 10x higher learning rate, in addition to Dropout being added. This means the comparison below shows what happened between two different configurations, not the isolated effect of Dropout. A rigorous ablation would hold the learning rate constant. This is flagged directly rather than presenting a confounded result as clean evidence for Dropout's effect. + +## Training curves + +**Baseline:** + +![Baseline loss curve](images/mlp-baseline-loss.png) + +**With Dropout (and higher learning rate):** + +![Dropout loss curve](images/mlp-dropout-loss.png) + +## Where each model gets it wrong + +**Baseline misclassifications:** + +![Baseline misclassified digits](images/mlp-baseline-misclassified.png) + +**Dropout version misclassifications:** + +![Dropout misclassified digits](images/mlp-dropout-misclassified.png) + +## Results + +| Model | Test loss | +|---|---| +| Baseline (no dropout, lr=0.001) | 0.279 | +| Dropout (0.2, lr=0.01) | 0.099 | + +## Notebooks + +- [`mnist-mlp-pred.ipynb`](../mnist-mlp-regularization/mnist-mlp-pred.ipynb) +- [`mnist-mlp-pred-dropout.ipynb`](../mnist-mlp-regularization/mnist-mlp-pred-dropout.ipynb) diff --git a/docs/images/autoencoder-denoising-result.png b/docs/images/autoencoder-denoising-result.png new 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digit classification. + +## Architecture + +Both models share the same base architecture: + +``` +Dense(500, relu) โ†’ Dense(100, relu) โ†’ Dense(10, softmax) +``` + +The dropout version adds `Dropout(0.2)` after each hidden layer. + +## โš ๏ธ Honest note on this comparison + +This is **not a clean ablation** โ€” two things changed between the baseline and dropout runs, not one: + +| | Baseline | Dropout version | +|---|---|---| +| Dropout | None | 0.2 after each hidden layer | +| Optimizer | SGD, lr=0.001 | SGD, lr=0.01 | + +The learning rate is 10x higher in the dropout run. That means the test loss improvement below **can't be attributed to Dropout alone** โ€” the higher learning rate is a confound. Both notebooks are included as-is because they reflect real experimentation, but the results should be read as "these two configurations produced these two outcomes," not as proof that Dropout alone caused the improvement. A clean ablation would hold the learning rate fixed and vary only Dropout. + +## Results + +| Model | Test loss (categorical crossentropy) | +|---|---| +| Baseline (no dropout, lr=0.001) | 0.279 | +| Dropout (0.2, lr=0.01) | 0.099 | + +## Tech stack + +TensorFlow / Keras, NumPy, matplotlib + +## Notebooks + +- [`mnist-mlp-pred.ipynb`](mnist-mlp-pred.ipynb) โ€” baseline +- [`mnist-mlp-pred-dropout.ipynb`](mnist-mlp-pred-dropout.ipynb) โ€” with Dropout diff --git a/mnist-mlp-regularization/mnist-mlp-pred-dropout.ipynb b/mnist-mlp-regularization/mnist-mlp-pred-dropout.ipynb new file mode 100644 index 0000000..3b0a270 --- /dev/null +++ b/mnist-mlp-regularization/mnist-mlp-pred-dropout.ipynb @@ -0,0 +1,443 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 15, + "id": "fe7570b6-1e0d-480f-b6cc-17868ec7ea43", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import datasets,utils,models,layers,optimizers,losses\n", + "import numpy as np \n", + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "markdown", + "id": "bf9f047c-ffaf-4cf8-973f-21b8f0bcba83", + "metadata": {}, + "source": [ + "# data preparation " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "91befa7a-0369-41c5-a042-e0256b97b76a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "(train_images , train_labels), (test_images,test_labels ) = datasets.mnist.load_data()\n", + "\n", + "# preprocessing\n", + "X_train = train_images.reshape(60000,784)\n", + "X_test = test_images.reshape(10000,784)\n", + "\n", + "# Normalizing (0,1 )\n", + "X_train = X_train.astype('float') / 255\n", + "X_test = X_test.astype('float') / 255\n", + "\n", + "y_train = utils.to_categorical(train_labels)\n", + "y_test = utils.to_categorical(test_labels) \n", + "\n", + "print(train_labels[0])\n", + "print(y_train[0])\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3c80e2f2-1805-4dcb-918d-f7ab72f95a38", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#visualize Sample Traning Data \n", + "for i in range(5):\n", + " plt.subplot(1,5,i+1)\n", + " plt.imshow(train_images[i],cmap='gray')\n", + " plt.title(train_labels[i])\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "markdown", + "id": "5e6329a7-76c8-4851-944d-63c83046a8cc", + "metadata": {}, + "source": [ + "# model creation" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d538ccae-1104-4be7-ae82-abff974847d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    Model: \"sequential_2\"\n",
+       "
    \n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_2\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
    โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                         โ”ƒ Output Shape                โ”ƒ         Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ dense_6 (Dense)                      โ”‚ (None, 500)                 โ”‚         392,500 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dropout_2 (Dropout)                  โ”‚ (None, 500)                 โ”‚               0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_7 (Dense)                      โ”‚ (None, 100)                 โ”‚          50,100 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dropout_3 (Dropout)                  โ”‚ (None, 100)                 โ”‚               0 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_8 (Dense)                      โ”‚ (None, 10)                  โ”‚           1,010 โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+       "
    \n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ dense_6 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m) โ”‚ \u001b[38;5;34m392,500\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_7 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) โ”‚ \u001b[38;5;34m50,100\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_8 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m1,010\u001b[0m โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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     Trainable params: 443,610 (1.69 MB)\n",
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    \n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m443,610\u001b[0m (1.69 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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0.1242 - val_loss: 0.1094\n", + "Epoch 46/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.1218 - val_loss: 0.1082\n", + "Epoch 47/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.1194 - val_loss: 0.1071\n", + "Epoch 48/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.1181 - val_loss: 0.1062\n", + "Epoch 49/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.1147 - val_loss: 0.1048\n", + "Epoch 50/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.1152 - val_loss: 0.1041\n" + ] + } + ], + "source": [ + "history = mymodel.fit(X_train,y_train,batch_size=128,epochs=50,validation_split=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5faabfcd-bd9c-4db2-870e-ac838ff72265", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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IiEg1DDcysBg5QzEREZFaGG5kwHluiIiI1MNwIwMr57khIiJSDcONDCxcfoGIiEg1DDcyCOGwFBERkWoYbmTgXxWcw1JERESKY7iRgX9VcN4tRUREpDiGGxlYvT03XH6BiIhIeQw3MvD13DhcHrjcHpVbQ0RE1L0w3MjAV3MDcAkGIiIipTHcyMCo00KvFQBw8UwiIiKlMdzIxL94JouKiYiIFMVwIxPOdUNERKQOhhuZWLgEAxERkSoYbmTiWxmcc90QEREpi+FGJiGcpZiIiEgVDDcyYUExERGROhhuZGL11tzwVnAiIiJlMdzIxOqtueESDERERMpiuJGJL9zUcIZiIiIiRTHcyMR3Kzh7boiIiJTFcCOTEP+t4Aw3RERESmK4kYn/bikOSxERESmK4UYmVt88N+y5ISIiUhTDjUys7LkhIiJSBcONTCzenhvW3BARESmL4UYmXBWciIhIHQw3MmFBMRERkToYbmTiKyjm8gtERETKYriRiW+G4nq3CIeLvTdERERKUTXcbNq0CRMmTEBiYiIEQcCHH354zvds3LgR6enpMJlMuOCCC/Daa6/J39B2sOi1/sc1XBmciIhIMaqGG7vdjuHDh2Px4sVtOv/IkSMYP348xowZg127duFPf/oTZs+ejQ8++EDmlp4/nVYDo066vFyCgYiISDk6Nb88Ozsb2dnZbT7/tddeQ0pKChYtWgQASEtLw/bt2/G3v/0NkyZNkqmV7Rdi1MHhcnLxTCIiIgV1qZqbH374AVlZWU2OXXvttdi+fTvq6+tbfI/D4YDNZmuyKcU3142dRcVERESK6VLhpqioCHFxcU2OxcXFweVyobS0tMX3LFiwAOHh4f4tOTlZiaYCaDRLMYeliIiIFNOlwg0ACILQ5Lkoii0e95k3bx4qKyv9W0FBgext9LH6J/LjsBQREZFSVK25OV/x8fEoKipqcqykpAQ6nQ7R0dEtvsdoNMJoNCrRvGYsBs51Q0REpLQu1XOTkZGBdevWNTn21VdfYeTIkdDr9Sq16uy4BAMREZHyVA031dXVyMnJQU5ODgDpVu+cnBzk5+cDkIaUpkyZ4j9/xowZOHbsGObOnYu8vDy8/fbbeOutt/DYY4+p0fxz4hIMREREylN1WGr79u246qqr/M/nzp0LAJg6dSreeecdFBYW+oMOAKSmpmLt2rV45JFH8OqrryIxMRGvvPJKp7wNHGhYgoE9N0RERMpRNdxceeWV/oLglrzzzjvNjl1xxRXYuXOnjK0KHBYUExERKa9L1dx0NVYWFBMRESmO4UZGvpobLr9ARESkHIYbGfnuluLyC0RERMphuJGRhQXFREREimO4kZF/+QXW3BARESmG4UZGvrulani3FBERkWIYbmTkW36BPTdERETKYbiRUQjnuSEiIlIcw42M/AXFTlerkxUSERFR4DDcyMhXUCyKQG09e2+IiIiUwHAjI7NeC0GQHnNoioiISBkMNzLSaARY9FyCgYiISEkMNzKzGLkEAxERkZIYbmTGJRiIiIiUxXAjM/9cN+y5ISIiUgTDjcz8SzCwoJiIiEgRDDcysxo5SzEREZGSGG5kZvHPUsxwQ0REpASGG5mFGFhQTEREpCSGG5n5l2Bgzw0REZEiGG5k1lBQzHBDRESkBIYbmVl9NTccliIiIlIEw43MfHdLcfkFIiIiZTDcyMxi8C2/wJ4bIiIiJTDcyCzE13PDmhsiIiJFMNzIzNdzw5obIiIiZTDcyMzKW8GJiIgUxXAjM6t/VXCGGyIiIiUw3MjM6i8oZrghIiJSAsONzHw9N3X1Hrg9osqtISIiCn4MNzKzGLT+xxyaIiIikh/DjcyMOg20GgEAYOdcN0RERLJjuJGZIAiwentv7Oy5ISIikh3DjQL8d0yx54aIiEh2DDcK8NXd8I4pIiIi+THcKCCEc90QEREphuFGARbOdUNERKQYhhsF+JZgqOH6UkRERLJjuFGAr6CY60sRERHJj+FGAf6VwXm3FBERkewYbhTgm+eGBcVERETyY7hRgH9YiuGGiIhIdgw3CvAVFHNYioiISH4MNwpgQTEREZFyVA83S5YsQWpqKkwmE9LT07F58+ZWz1++fDmGDx8Oi8WChIQE3H333SgrK1Oote1jNfgm8WPPDRERkdxUDTcrV67EnDlz8MQTT2DXrl0YM2YMsrOzkZ+f3+L53333HaZMmYJ7770Xe/bswapVq7Bt2zZMnz5d4ZafHy6/QEREpBxVw83ChQtx7733Yvr06UhLS8OiRYuQnJyMpUuXtnj+1q1b0bt3b8yePRupqam47LLLcP/992P79u0Kt/z8cPkFIiIi5agWbpxOJ3bs2IGsrKwmx7OysvD999+3+J7MzEwcP34ca9euhSiKKC4uxn//+19cf/31Z/0eh8MBm83WZFOaxch5boiIiJSiWrgpLS2F2+1GXFxck+NxcXEoKipq8T2ZmZlYvnw5br/9dhgMBsTHxyMiIgL/+Mc/zvo9CxYsQHh4uH9LTk4O6O9oC988N7wVnIiISH6qFxQLgtDkuSiKzY757N27F7Nnz8ZTTz2FHTt24IsvvsCRI0cwY8aMs37+vHnzUFlZ6d8KCgoC2v628N0tVcOeGyIiItnp1PrimJgYaLXaZr00JSUlzXpzfBYsWIDRo0fj8ccfBwAMGzYMVqsVY8aMwXPPPYeEhIRm7zEajTAajYH/AefBd7eU0+2B0+WBQad6piQiIgpaqv2VNRgMSE9Px7p165ocX7duHTIzM1t8T01NDTSapk3WaqUhH1EU5WloAFi8k/gBLComIiKSm6pdCHPnzsWbb76Jt99+G3l5eXjkkUeQn5/vH2aaN28epkyZ4j9/woQJWL16NZYuXYrDhw9jy5YtmD17Ni655BIkJiaq9TPOSa/V+Htr7JzrhoiISFaqDUsBwO23346ysjI8++yzKCwsxJAhQ7B27Vr06tULAFBYWNhkzptp06ahqqoKixcvxqOPPoqIiAhcffXV+Mtf/qLWT2gzq0ELp8vDWYqJiIhkJoideTxHBjabDeHh4aisrERYWJhi33vZX9bjeEUt1jyYiREpkYp9LxERUTA4n7/frGxViK+omHPdEBERyYvhRiG+omLOdUNERCQvhhuFcAkGIiIiZTDcKKRh8UwOSxEREcmJ4UYhvpqbGt4tRUREJCuGG4X4lmDgPDdERETyYrhRiL+gmD03REREsmK4UYh/WIoFxURERLJiuFGIf1iKBcVERESyale4effdd/HZZ5/5n//P//wPIiIikJmZiWPHjgWsccHEauCwFBERkRLaFW6ef/55mM1mAMAPP/yAxYsX48UXX0RMTAweeeSRgDYwWDQUFDPcEBERyaldC2cWFBSgb9++AIAPP/wQv/nNb3Dfffdh9OjRuPLKKwPZvqBh9RcUc1iKiIhITu3quQkJCUFZWRkA4KuvvsK4ceMAACaTCbW1tYFrXRCxGNhzQ0REpIR29dxcc801mD59OkaMGIH9+/fj+uuvBwDs2bMHvXv3DmT7goZ/+QX23BAREcmqXT03r776KjIyMnDq1Cl88MEHiI6OBgDs2LEDv/3tbwPawGBhYUExERGRItrVcxMREYHFixc3O/7MM890uEHBqnFBsSiKEARB5RYREREFp3b13HzxxRf47rvv/M9fffVVXHjhhbjzzjtRUVERsMYFE1+48YiAw+VRuTVERETBq13h5vHHH4fNZgMA5Obm4tFHH8X48eNx+PBhzJ07N6ANDBZmvdb/uJpDU0RERLJp17DUkSNHMGjQIADABx98gBtuuAHPP/88du7cifHjxwe0gcFCqxFg1mtRW++WiopD1G4RERFRcGpXz43BYEBNTQ0A4Ouvv0ZWVhYAICoqyt+jQ835hqbYc0NERCSfdvXcXHbZZZg7dy5Gjx6Nn376CStXrgQA7N+/Hz179gxoA4OJ1ahFaTUXzyQiIpJTu3puFi9eDJ1Oh//+979YunQpkpKSAACff/45rrvuuoA2MJg0TOTHuW6IiIjk0q6em5SUFHz66afNjr/00ksdblAwCzFyrhsiIiK5tSvcAIDb7caHH36IvLw8CIKAtLQ03HTTTdBqted+czfl77lhuCEiIpJNu8LNwYMHMX78eJw4cQIDBgyAKIrYv38/kpOT8dlnn6FPnz6BbmdQ8C/BwGEpIiIi2bSr5mb27Nno06cPCgoKsHPnTuzatQv5+flITU3F7NmzA93GoOFbgoF3SxEREcmnXT03GzduxNatWxEVFeU/Fh0djRdeeAGjR48OWOOCjdXfc8NwQ0REJJd29dwYjUZUVVU1O15dXQ2DwdDhRgUrq7+gmMNSREREcmlXuLnhhhtw33334ccff4QoihBFEVu3bsWMGTNw4403BrqNQYMFxURERPJrV7h55ZVX0KdPH2RkZMBkMsFkMiEzMxN9+/bFokWLAtzE4GH11tywoJiIiEg+7aq5iYiIwEcffYSDBw8iLy8Poihi0KBB6Nu3b6DbF1S4/AIREZH82hxuzrXa94YNG/yPFy5c2O4GBTMWFBMREcmvzeFm165dbTpPEIR2NybY+W4FZ0ExERGRfNocbr799ls529Et+Cbxs7PnhoiISDbtKiim9mm4W4o9N0RERHJhuFFQCGtuiIiIZMdwoyCLseFWcI9HVLk1REREwYnhRkFWQ0OJU009h6aIiIjkwHCjIJNeA433ZjLOUkxERCQPhhsFCYLg771huCEiIpIHw43Cwsx6AEBJlUPllhAREQUnhhuFXZgcAQDYdqRc3YYQEREFKYYbhV16QRQAYOuRMpVbQkREFJwYbhQ26oJoAMCOYxVwujwqt4aIiCj4MNworF9sCKKsBtTVe5B74rTazSEiIgo6qoebJUuWIDU1FSaTCenp6di8eXOr5zscDjzxxBPo1asXjEYj+vTpg7fffluh1nacIAgYleodmjrMuhsiIqJAUzXcrFy5EnPmzMETTzyBXbt2YcyYMcjOzkZ+fv5Z33Pbbbfhm2++wVtvvYV9+/ZhxYoVGDhwoIKt7riGcMO6GyIiokATRFFUbR2AUaNG4aKLLsLSpUv9x9LS0jBx4kQsWLCg2flffPEF7rjjDhw+fBhRUVHt+k6bzYbw8HBUVlYiLCys3W1vpuIYsPZxwFkN3L221VPzCm3IfnkzLAYtdj+dBb1W9Q40IiKiTu18/n6r9lfV6XRix44dyMrKanI8KysL33//fYvv+fjjjzFy5Ei8+OKLSEpKQv/+/fHYY4+htrb2rN/jcDhgs9mabLIwhgIHvgSObQEcVa2eOiAuFBEWPWqcbuSeqJSnPURERN2UauGmtLQUbrcbcXFxTY7HxcWhqKioxfccPnwY3333HX755ResWbMGixYtwn//+1/MnDnzrN+zYMEChIeH+7fk5OSA/g4/SxQQEi89PrWv1VM1GgGX9JZ6nn5k3Q0REVFAqT4eIghCk+eiKDY75uPxeCAIApYvX45LLrkE48ePx8KFC/HOO++ctfdm3rx5qKys9G8FBQUB/w1+sWnSvmTvOU/13RLOuhsiIqLAUi3cxMTEQKvVNuulKSkpadab45OQkICkpCSEh4f7j6WlpUEURRw/frzF9xiNRoSFhTXZZBM7SNqX5J3zVN9kftuPlsPl5nw3REREgaJauDEYDEhPT8e6deuaHF+3bh0yMzNbfM/o0aNx8uRJVFdX+4/t378fGo0GPXv2lLW9bRLnDTfFe8556sD4MISZdLA73dhzUqY6ICIiom5I1WGpuXPn4s0338Tbb7+NvLw8PPLII8jPz8eMGTMASENKU6ZM8Z9/5513Ijo6GnfffTf27t2LTZs24fHHH8c999wDs9ms1s9o4B+WOnfPjVYj4BLeEk5ERBRwqoab22+/HYsWLcKzzz6LCy+8EJs2bcLatWvRq1cvAEBhYWGTOW9CQkKwbt06nD59GiNHjsTkyZMxYcIEvPLKK2r9hKZ6eOfbsZcA9tJznn6pt+7mRy6iSUREFDCqznOjBtnmufF5eThQcRSY+imQOqbVU3OPV2LC4u8QatQh5+ksaDUtF1ITERF1d11inpugFTtY2rfhjqlBiWEINepQ5XBhL+tuiIiIAoLhJtDO43ZwrUbAxd66mx+PsO6GiIgoEBhuAu08iooBcBFNIiKiAGO4CbTGc920oZzJV1T805EyuD3dqvyJiIhIFgw3gRbdF9DoAYcNqGx5YsHGBieGIcSog63OhV+LWHdDRETUUQw3gaYzADH9pMdtGJrSaTVI7xUJgENTREREgcBwI4fzKCoGGs13w8n8iIiIOozhRg7nGW5GedeZ+uloOTysuyEiIuoQhhs5nMdcNwAwNCkcFoMWp2vqsa+4SsaGERERBT+GGzn4em5O7QfcrnOerm9Ud8OhKSIioo5huJFDRC9AbwHcDqDiSJvewnWmiIiIAoPhRg4aTcMimsV72vSWSy/wzVRcjm623BcREVFAMdzIJa7RZH5tMDQpAia9BuV2Jw6UVMvYMCIiouDGcCMX/0zFbSsqNuhYd0NERBQIDDdyOc81pgDg0lSp7oaT+REREbUfw41cfD035YeA+to2vWWUv6i4jHU3RERE7cRwI5eQOMAcBYgeoHR/m94yPDkcRp0GpdVOHDrFuhsiIqL2YLiRiyA0XSG8DYw6LS5K4TpTREREHcFwI6fzXIYBaFiKgfPdEBERtQ/DjZx84ab4PMKNv6iYdTdERETtwXAjpzjfGlNtv2NqREoEDDoNTlU5cKTULlPDiIiIghfDjZx8sxTbjgN1lW16i0mvxYjkCADA578UydQwIiKi4MVwIydzBBCWJD0+j96bOy5JBgC8vvEQKmvqZWgYERFR8GK4kVs7iopvHJ6E/nEhsNW58PqmQzI1jIiIKDgx3MjtPG8HBwCtRsBjWQMAAMu2HEVJVZ0cLSMiIgpKDDdya0e4AYBrBsVhREoEauvdWLz+oAwNIyIiCk4MN3Lz3w6+BziPW7sFQcDj10q9Nyt+ykdBeY0crSMiIgo6DDdy6zEAEDRAbTlQXXJeb83sE4Mx/WJQ7xbx0rq2LeFARETU3THcyE1vBqIukB6fR1Gxj6/3Zk3OCewrqgpky4iIiIISw40S/HdMnV/dDQAM6xmB7CHxEEXgb1/tC3DDiIiIgg/DjRL8RcV72vX2R7P6QyMA6/YWY2d+RQAbRkREFHwYbpTQgZ4bAOgbG4pJF/UEAPz1i31cc4qIiKgVDDdKiPWtMfUr4PG06yPmXNMfBq0GPxwuw3cHSwPYOCIiouDCcKOEqAsArQGotwOV+e36iKQIMyZfmgIA+OuX7L0hIiI6G4YbJWh1QIx01xOKz/+OKZ+ZV/WFxaDFz8cr8QUX1SQiImoRw41S2rHG1JliQoyYflkqAOnOKZe7fUNcREREwYzhRilx7VuG4UzTL78AERY9Dp2yY/WuEwFoGBERUXBhuFGK/3bw9vfcAECYSY8Hr+wDAHj56wNwuNwdbRkREVFQYbhRim9YqnQ/4HJ26KOmZPRGfJgJJ07X4s3NRwLQOCIiouDBcKOU8GTAEAp4XED5oQ59lEmvxdys/gCAhev247sDvDWciIjIh+FGKYIQkKJin1vTe+KWi5Lg9oiY+Z+dOFZm7/BnEhERBQOGGyX5wk0Hbgf3EQQBz988FMN7hqOyth73vbcDdoerw59LRETU1THcKCk2MHdM+Zj0Wrx+10j0CDViX3EV5v5fDjweTu5HRETdG8ONkgI4LOUTH27Ca79Lh0GrwZd7ivGP9QcD9tlERERdkerhZsmSJUhNTYXJZEJ6ejo2b97cpvdt2bIFOp0OF154obwNDKQ47xpTFUcBZ+BqZNJ7ReK5iUMAAC99vR9f7uHsxURE1H2pGm5WrlyJOXPm4IknnsCuXbswZswYZGdnIz+/9fWXKisrMWXKFIwdO1ahlgaINQYIiQMgAge/CehH33ZxMqZl9gYAzF2Zg31FVQH9fCIioq5C1XCzcOFC3HvvvZg+fTrS0tKwaNEiJCcnY+nSpa2+7/7778edd96JjIwMhVoaQBdOlvYbX2z3CuFn88T1aci4IBp2pxu/f287Ttd0bD4dIiKirki1cON0OrFjxw5kZWU1OZ6VlYXvv//+rO9btmwZDh06hKeffrpN3+NwOGCz2Zpsqsp8SJrvpjgX+PXTgH60XqvBq5MvQs9IM/LLa/DQil1cf4qIiLod1cJNaWkp3G434uLimhyPi4tDUVHLNSMHDhzAH//4Ryxfvhw6na5N37NgwQKEh4f7t+Tk5A63vUMsUcCo+6XHG14IeO9NlNWAN+4aCbNei80HSvHC578G9POJiIg6O9ULigVBaPJcFMVmxwDA7XbjzjvvxDPPPIP+/fu3+fPnzZuHyspK/1ZQUNDhNndYxkzAGAaU7AHyPg74xw9KDMPfbh0OAHjzuyNYua31GiYiIqJgolq4iYmJgVarbdZLU1JS0qw3BwCqqqqwfft2zJo1CzqdDjqdDs8++yx2794NnU6H9evXt/g9RqMRYWFhTTbVWaKAUTOkxxv/EvDeGwC4flgCHrq6LwDgDx/k4qV1+yGKnAOHiIiCn2rhxmAwID09HevWrWtyfN26dcjMzGx2flhYGHJzc5GTk+PfZsyYgQEDBiAnJwejRo1SqumBkfEgYAyX5rzZ+6EsX/HIuP6YflkqAODlbw5g1opdqKvnKuJERBTc2la4IpO5c+firrvuwsiRI5GRkYE33ngD+fn5mDFD6tWYN28eTpw4gffeew8ajQZDhgxp8v7Y2FiYTKZmx7sEcyRw6QPAxhek3ptBNwEabUC/QqMR8OQNg9AvLgRPfvgLPvu5EAXlNfjnlJGICzMF9LuIiIg6C1Vrbm6//XYsWrQIzz77LC688EJs2rQJa9euRa9evQAAhYWF55zzpku79AGp9+bUr8CeNbJ9ze0Xp+Bf945CpEWPn49X4sbF3yH3eKVs30dERKQmQexmhRg2mw3h4eGorKzsHPU3G/4CbHgeiOkPPLg14L03jeWX1eDed7fhQEk1THoNFt52IcYPTZDt+4iIiALlfP5+q363VLd36QzAFA6U7gd+WS3rV6VEW/DBg5m4ckAP1NV78ODynXjlmwMsNCYioqDCcKM2U7g0sR/gvXNK3oLfMJMeb029GPd6C40XrtuP2e/nsNCYiIiCBsNNZ3DJ/VKBcdkBIPe/sn+dViPgzzcMwgu3DIVOI+CT3Sdx2+s/oKC8RvbvJiIikhvDTWdgCmvae+N2KfK1d1wiFRpHeAuNr39lM77iiuJERNTFMdx0FpfcB5ijgPJDQO4qxb42o080Pps9BiNSImCrc+G+f+3A//t0L5wurklFRERdE8NNZ2EMBUbPlh5velGx3hsASIowY+V9Gf4J/9767ghue/0HHK/gMBUREXU9DDedycW/ByzRQPlh4OeVin61QafBkzcMwht3pSPMpENOwWlc/8p3+HpvsaLtICIi6iiGm87EGAKMflh6vOlFwF2veBOyBsfjs9ljMDw5ApW19Zj+3nY8vzYP9W4OUxERUdfAcNPZXDwdsPYAKo4C3zyrShOSoyxYdX8G7h7dGwDwxqbDuP31H3DydK0q7SEiIjofDDedjcEKZP2v9Pj7V4DNf1enGToNnp4wGK/9Lh2hJh125p/GdYs24dVvD8LuUK4eiIiI6Hwx3HRGw28Hsp6THn/zLPDTP1VrynVD4rF29hgM7xkOW50Lf/1yH8a8+C3e2HQItU5O/EdERJ0P15bqzNY/B2z6q/T45teB4Xeo1hS3R8Qnu09i0df7cbRMuouqR6gRM6/sg9+OSoFRJ9+aWEREROfz95vhpjMTReDzPwA/vQ4IWuC294C0G1RtksvtwepdJ/DKNwdwvEKqwUkIN2HW1X1xa3oyDDp2BhIRUeAx3LSiS4UbAPB4gI9nATnLAa0BuPP/gD5Xqd0qOF0erNpRgMXrD6Kwsg4A0DPSjNlj++GWEUnQaRlyiIgocBhuWtHlwg0gTej332lA3ieA3gJM+QhIvkTtVgEA6urdWPFTPl799hBKqx0AgOQoMx68si8mXdSTPTlERBQQDDet6JLhBgBcDmDFHcCh9dJK4tM+A+KHqt0qv1qnG//aehSvbzyMMrsTgDRcNeOKPrj94mSY9KzJISKi9mO4aUWXDTcA4LQD/7oFKNgqzYVz9xdATF+1W9VErdON//yUj9c3HkJJldST0yPUiPsvvwB3jkqBxaBTuYVERNQVMdy0okuHGwCoPQ28ewNQlAuE9QSmfQpEpardqmbq6t1YteM4XttwCCe8k/9FWQ2497JUTMnohVCTXuUWEhFRV8Jw04ouH24AoPoUsCwbKDsAGMOBCS8BQyap3aoWOV0erNl1HK9+ewj55dIt5OFmPSZd1BO3XJSEwYlhEARB5VYSEVFnx3DTiqAINwBgOwn83xTg+Dbp+YWTgey/SKuLd0Iutwef/HwSi9cfxKFTdv/xAXGhmJSehJsuTEJcmEnFFhIRUWfGcNOKoAk3gLSw5sYXgc1/A0QPEHUBMOlNICld7ZadldsjYuP+Enyw8wTW7S2G0yUtyKkRgMv69cCki5KQNSgeZgMLkImIqAHDTSuCKtz4HN0CrL4PsB0HNDrg6ieBzIcBTee+Dbuyph6f5RZi9c7j2H6swn88xKjD+KHxuHlET4xKjYJGw2ErIqLujuGmFUEZbgCgtgL45GFg70fS89TLpSUbwhLVbVcbHSuzY/XOE1i96zgKyhtWH0+KMOOmCxNxy0VJ6BvbOYfciIhIfgw3rQjacANIyzXs+jfw+f8A9TWAORK4cbHqSzacD1EUsf1YBT7YcRyf5Raiqq5hBfJhPcNx84gkTBieiJgQo4qtJCIipTHctCKow41P6QHgg3uBwt3S82F3AFc/AUSkqNuu81RX78Y3eSVYs+s4Nuw7BZdH+ldVqxFwRf8euHlEEq4ZFMcJAomIugGGm1Z0i3ADAC4nsP7/Ad+/Ij3XGoBL7gPGPApYotRtWzuUVTvwye6TWLPrBHYfr/QfDzfrcfvFyfjdqF5Iibao2EIiIpITw00ruk248TmxA/h6PnBkk/TcGAaMng1c+iBgsKratPY6WFKND3edwJpdJ/wTBAoCcPWAWEzJ7I0xfWNYhExEFGQYblrR7cININXiHFovhZyin6VjIXHAFX8ALpoCaLvmbMFuj4gN+0rw7g/HsGn/Kf/x1Bgr7rq0F34zsifCOBMyEVFQYLhpRbcMNz4eD7BntTRcVXFUOhbVR7p1fPDNUvdHF3X4VDX+tfUY/rv9OKocUhGyxaDFzSOScMfFKRiYEAq9tnPfGk9ERGfHcNOKbh1ufFxOYOe7wMa/AHZvj0f8UCBzthRyumhPDgDYHS6s2XUC7/1wFPuLq/3HDVoN+saGIC0hDGkJoUhLCMPA+FBE864rIqIugeGmFQw3jTiqgR9elYqOnd4gEJYEjLofuGgqYI5QtXkdIYoith4ux7+2HsXm/aX+3pwzxYYakZYQhsGJYcgaHI/hPcO51hURUSfEcNMKhpsW1JQD298GfnoDqC6WjhlCpHqcUTOAyF7qtq+DRFHE8Ypa5BXakFdYhbxCG34tsuFoWU2zc5OjzLhhWCImDEtEWkIogw4RUSfBcNMKhptWuBxA7iqpN6dkr3RM0ACDbgIyHgJ6dt41q9rD7nDh16Iq/Fpkw9bD5fgmrxg1Trf/9T49rJgwPBE3DEtE39gQFVtKREQMN61guGkDUQQOfQN8vxg4/G3D8Z6XAEMmAYNu7DLLOpyPWqcb638twSe7T2L9vhL/op4AkJYQhhuGJeCqAbEYGB/KW82JiBTGcNMKhpvzVPSL1JOTuwrw1DccTx4l9eik3QhEJKvXPplU1dXj67xifLK7EJv2N8yODEgTB45KjUJGn2hcekE0BsQx7BARyY3hphUMN+1UVQT8slpamLNga9PXkkZKQWfQjUBkb1WaJ6fTNU58uacIn/9ShG1HymFvNHQFAJEWPUalRuPSC6JwaZ9o9I9l2CEiCjSGm1Yw3ASA7SSQ94kUdI59D6DRv0IJFwLDbgOG/AYIjVOrhbJxuT345aQNPxwqw9bDZdh2tLxJnQ4ghZ2RvaMwKjUKl6RGYVBCGHScY4eIqEMYblrBcBNgVUWNgs4WQPTWqQgaoM/VwLDbgYHXd9mlHs6l3u1B7olKbD1chq2Hy7G9hbBjNWhxUa9IjEqNwsW9ozA8OYKLfRIRnSeGm1Yw3MioukQKObvfB05sbzhuCAHSJkg9OqlXAJrg/cNe7/bglxOV2Ha0HD8dkTZbXdM5dgxaDYYkhWFoUjgGJ4VjaFI4+saGcAZlIqJWMNy0guFGIWWHgJ9XSptvqQcACE0Ahv4GGHA90HNkl54NuS08HhH7iquw7Wg5fvSGnVNVjmbnGXQapMWHYkhSOIZ4A0//uFAYdAw8REQAw02rGG4UJopAwU/Az+9LBcl1pxteM4YBqZcDfa6ShrCiLlCtmUoRRRFHy2rw8/HTyD1eiV9OVmLPCVuLMyib9Vpc1i8G49JicdXAWMSGmlRoMRFR58Bw0wqGGxW5nMDBdVLIOfwtUFPW9PXI3kCfsVLQSR0DmMJVaabSPB4R+eU1yD0hhZ1fTlTilxM2VNbWNznvwuQIjEuLxdi0OAyM5+zJRNS9MNy0guGmk/B4gKLdwMFvgEPfSreXexr1XghaIHEEkHIp0CsTSMkALFHqtVdhoihib6EN3+SV4Ou8Yvx8vLLJ60kRZoz19ugMiAtFfJiJt58TUVBjuGkFw00n5agCjn4HHFovBZ7yQ83PiRkA9MoAUjKl0BORAnST3otiWx3W/1qCr/cW47uDpXA0mj0ZkGp2ekVZ0Cvait7RFvSOsaJ3tBW9oi1IjDBDy+BDRF0cw00rGG66iNP50hw6+T8Ax34ASvc1PycsSZopuefF0pYwDNAZlW+rwmqdbmw5WIqv84rx05Fy5JfXNJlB+UwGrQb940Mw1FusPCwpAv3jQ2DUBe9da0QUfLpUuFmyZAn++te/orCwEIMHD8aiRYswZsyYFs9dvXo1li5dipycHDgcDgwePBjz58/Htdde2+bvY7jpouxl0tDVse+B/K1AYU7TYSwA0BqA+GHesDNS2neD3h2X24PCyjocLbPjaFkNjpVK+6NlduSX1cDp9jR7j14rYEB8qD/w+O7O4vw7RNRZdZlws3LlStx1111YsmQJRo8ejddffx1vvvkm9u7di5SUlGbnz5kzB4mJibjqqqsQERGBZcuW4W9/+xt+/PFHjBgxok3fyXATJJx24Ph24Pg27/6n5gXKAGCNlYJO0kVAUrpUx2OOVL69KnF7RJw8XYtfTlQi17v9cqISFTX1zc7VagT0jrZgYHwYBsSHon9cKAbGhyIlysJ6HiJSXZcJN6NGjcJFF12EpUuX+o+lpaVh4sSJWLBgQZs+Y/Dgwbj99tvx1FNPtfi6w+GAw9Ewr4jNZkNycjLDTbARRWk+HX/g2QYU/dy8dwcAovt6g4438MQPBfTd5zZrURRxvKJp4Mk9UYnTLQQeQLolvX9cCAbEhyI1JgTx4UbEhZkQH2ZCfLgJFoNO4V9ARN3R+YQb1f6r5HQ6sWPHDvzxj39scjwrKwvff/99mz7D4/GgqqoKUVFnv4tmwYIFeOaZZzrUVuoCBAGISpW2YbdKx+prgcLdwIkdDVvFUaDsoLT9vFI6T6MD4gZL62IlDJf2cYMAvVmlHyMvQRCQHGVBcpQF2UMTAEiBp6TKgV+LqrCvyIZfi6qwv7gKB4qrUVvvxu7jldh9xh1bPqEmnT/oxIWZ0DPSjIHxoRgYH8ZeHyJShWrhprS0FG63G3FxTRdXjIuLQ1FRUZs+4+9//zvsdjtuu+22s54zb948zJ071//c13ND3YDeLN1VlXJpwzF7GXByV9PAU1MqhaDC3Q3nCVqgx0Bv2PFu8UMAY6jyv0MBgiAgLkwKJ1f07+E/7vaIOFpmx76iKvxaVIXj5TUostWhyFaH4so62J1uVNW5UFVXjQMl1c0+16zXYkB8qDfshGJgQhgGxociwmJQ8ucRUTejen/ymRORiaLYpsnJVqxYgfnz5+Ojjz5CbGzsWc8zGo0wGoP/DhpqI2s00G+ctAHScNbpfODkTqDwZ2/IyZHqd0r2SNvu/3jfLEizKMcPAeKGSsNZ8UOku7aCtGhZqxHQp0cI+vQIwXhvL09jVXX1KLbVoajSIQUeWx2Oltqxr7gK+4qqUFvvRk7BaeQUnG7yvrgwI1KiLOgZaUHPSLN3syApwoyECBPv5CKiDlEt3MTExECr1TbrpSkpKWnWm3OmlStX4t5778WqVaswbtw4OZtJwU4QgMhe0jb4ZumYKAK2kw29Ob6t6qQ0/075IWmBUB9zJBA3xBt2hgI9BgDR/QBT8Nd0hZr0CDXp0Te2eY+Wr9fn18Iq/FpkQ553f7yiFsU2B4ptDmw7WtHsfYIAxIVKw1u9Y6zecGVFn9gQpERZuMAoEZ2T6gXF6enpWLJkif/YoEGDcNNNN521oHjFihW45557sGLFCkycOPG8v5N3S1G7VZ8CinOBol+Aolyg+Bfg1D5AdLd8fkicVLzs22L6SaEnslfQLxjamqq6ehwsqcbxilqcOF2L4xU1OF5R691qUFff/NZ1H51GQEq0BX16hOCCHlLwiQszIdys929hJh10DEBEQafL3C3luxX8tddeQ0ZGBt544w3885//xJ49e9CrVy/MmzcPJ06cwHvvvQdACjZTpkzByy+/jFtuucX/OWazGeHhbVuHiOGGAqq+Djj1qxR0fKGn7ABQXXz292h00vBWbBoQO1gqXo4dJK2tpenewzGiKKLc7sTxilrkl9fgSKkdh05V4/ApaV/jPEuQPEOIUYcwkw5h3sATH25C/7hQ7xaC5EgWOhN1NV0m3ADSJH4vvvgiCgsLMWTIELz00ku4/PLLAQDTpk3D0aNHsWHDBgDAlVdeiY0bNzb7jKlTp+Kdd95p0/cx3JAi6iqBskMNd2aVHpBCT9khoL6m5ffozNKQVtxgKez0GCjd/RWR0q17enxEUUSRrc4fdA6VVONwqR2l1U7YautRWVuP6hZWV2+JSa9B39gQ9I8NRf94KfBcEBOCmFAjrAYtFyUl6oS6VLhRGsMNqcpXz3PqV6BkL1C8V9qf+hVw1bX8HkEDhPcEIr23up+5D9I7uNrD5fbAVudCpTfs+LaC8hocKK7C/uJqHDxVDafr7ENfJr0G0VYjYkKNiLEaEB1iQEyIEdEhRsSEGBBhMSDCrEekxYAIqx6hRh3DEJECGG5awXBDnZLHDZQfkYKObzu1X5qXx1Xb+nvNUUBEstTDE9FL2of7nqd0i8Lm8+H2iMgvr8G+oiop8JRU40BxFY6V1aC2vm3DXo1pNQIizHqEW6TAE2nRo2ekxb+AaWqMFUkRZtYBEXUQw00rGG6oSxFFoKoIqDgihZ8z97Xl5/4MUzgQngKEJ0k9QGFJUvjxPQ9N4LCXV43ThbJqJ05VO1BW7URptQNl1Q6U+h87cbq2HqdrnDhdU9/mMKTXCkiObFitPTVGug0+LsyEhHATIix69v4QnQPDTSsYbiio1NmAygJprp4zt8qCltfbOpOgAULipaATkewNPj0beoDCe7L35yzq6t2orK3H6Zp6VHgDT7ndiWPldhwtteNoqbSAqaOVYTAAMOo0iA+XlrRICDchLtyEhDATEiKkOYCSoywIMzGAUvfGcNMKhhvqVhzVUsipPOHdHwdsJ6R9ZYFU/+N2nvtzfL0/YYlAaJx0m7tvC40HQmKlgNSN1uhqK49HRKF3csMjpd7QU2bHydPSTM/l9jZcfwBhJh16RlqQHCVNeJjsnfgwIcLkrwOysBiaghjDTSsYboga8XgA+6mGsFNZAJz2hqDKfGlf23yivbMyhTcNPaHxUugJjW8UhOKkImj+EQYg9f6U2BworKyVlraorPPvT56uRUFFbZsDkE4jIMKiR5hZL9UBmfWIsBgQbtYj2mpAVIgB0VYDokOMiLJKj8NMet4WT10Cw00rGG6IzpOjSgo5pwuAqkJpDp/qYqkWyP+4GHA72v6Zegtg7SH1+FhjgZAe3n1s0+PWaMAYDmi6dzGu3eHyT3hYUN4w8WFBRQ2KKh2w1dbD6W596OtstBrBH3RCTTqEGHUIMemlvVGLEKMeIaaGx4kRJvSLC0WIUfXVe6ibYbhpBcMNkQxEEag7DVSXNIQe/75QCj/VRdLeWXV+ny1oAUsUYIn2bo0f+7YYKQhZYgBrTNCu6H42oiiirt6D07VOfw1QZW09Kr37ihonyu1OlNm9+2oHyuxOVNW1bV6gliRFmNE/LkSaJyhWmiCxb2wIzIbuPRElyYfhphUMN0Qqc9ql4GM/JYUhe4m0tIW9xPv8VMPe2Xyl8TbRW5uGHV9vUEhco733sTGs2w6ROVxuVNjrUWaX7gSrdrikrc7V8LjRc1ttPY6V1+BUVcu9dIIApERJC6BaDFqY9FqY9VrpsaHhsVmvhdmgg9WghcXbQ2Qx6GA16GAxamE16GDSa1g/RE0w3LSC4YaoC3E5gJpy6a6vxltthbS3l3qPlQJ2774tBdKN6UwNw2DmyHNsEYAhBDCGSAGqmw6XVdid2N9ojqB9RVU4UFLd5tqgthAEaRmNHqFGxIeZEBdmQmxYw+O4MCNiQ6VjXEW+e2C4aQXDDVEQE0WpRqhx2LGXNvQKVRc33TtsHfgyATBYG8KOIUQqlDaEeIfOWho+826miKAMRqXVDuwvqsKpagdqnW7UON2orXejrr7RY+/xmno3ahwu2J1u1DhdsDukfVvXD2vMatBKi6Z6t6YLqeoRbpYWUxVFESKkO9g8IuARRYjevUcEQkw6pERZ/L1PBl3w/TPqys7n7zcrwogoeAiCNCePKUxanPRcnDVNh8NqK86xnZaGykQPAFF67KwGznf0TNBIAcccIfUImSKa9g75jpnCvVuYNHxmCpf22s75n+6YECNi+ho79Bkej4jaejfsThdstS6UVNWhxOZAsa0Oxf59HYqrpOdOlwd2pxt2pxsnK8+yhEk7aAQgIdzsDzsp0dK+Z6QZ8eEm9AgxctbpTow9N0RE50MUpcVPnXapl8hZLc0n5KyWnjuqGobNWhpS61BvkZfe4g073tBjsJ6xhUjnNH7eODj5wpPO0PG2qEgUxSYF1La6pmuK2Wpd3n093B4RggBoBKHFvQDBvw5Zfvm5l+LQCFKYi2s0TOYbMusRaoRRr4FBq4Heuxl0gv+xXiu9ZtRrYNSxtqitOCzVCoYbIlKVyyktm9G4N6i2QrrbrMnjCml1+TqbtHfYzr6ifHv5Qo+vF8kQAhgsDcFI73tsaR6W/MNxjZ530h6l8yWKIk5VO/xB51iZtC8ol27BL6lywO0JzJ9OjQBvgbVUgN1QcC3tQ016RFqk+YoivOuXRXif+46HGnXdYq4ihptWMNwQUZflrpd6huoqGwKPo0oaXnNWS71JTjtQb2947OtZ8gWm2gopMEGG//TrTI3qkEIbApD/caO9b/MNtfmH3sKkczpxb4bbI6LM7kBxpTRMVmSrQ4l3X2RzoLTKgXq3x7uJcHofu7yPW1uVvj00AvyzVIdb9P5V66XHBkRapdojc6M71kzeANU4TBm0nbsXiTU3RETBSKtvKFbuCI9bCkdn9hw5q6WgVG/37r2b77HzjNDkC06eeulzXXXS1pY1zVojaLzhJ1yas0hv9vYimbx7sxSkfI8b9y75H1u9e3Ojx95NZ+xQeNJqBOlOrVAThiL8vN8viiJcHhF19VKRda2z0d732FuEbWu8dpl30dYKu3fx1tp61Djd8IhAuXcOo47QaQTvUJsRsY3uSPM/D5WG3LQaAW6PCI/3d3g8Itwe72NReqwRBAyID+1Qezr0W1T7ZiIiUodGG5iQ5ONyNg07vvqjJs9tjR776pMaDbnV2aS9xyUVbPt6p+QgaKTAozc3BCG9GdAavKFHaBp+Gh/T6BoVekc0etz4WFjT4HVGmBIEAXqtVIMT2sEFUevqvQGoth4VdinwVNbU43StExU1vnokJ2y1Ln9gku5ec/mDVL1b6sVzeURv71MdgI5d+7gwI37807gOfUZHMNwQEVHH6AyALgBhyVes7Qs6jipv71GdtHd59/W1jbaahmNOe9OeJv9ju/S6bw4k0SPNlO2sAuwd//nnJjT0QOl8PVEmqfdJa5B65LSGppvOtzdJ4UjX6D3+niszTDoTTHozYnVGwGoCwn3nR3j3pnPWQtW7PdIdag4XTlU5/HelldjqUFLVcKdaSVUdyuxO+IpZdBoBGo0ArSA0PNYI0AgCoqwdu2uuoxhuiIiocxCEhqJlJAT+8931ZwSh2obhNn/4EaWQ5atJEs/Yu50NvUr+7XTzY/W1DcN1EBvClhoErTfk6KXAozV6w5O012uN0OsMCNOZkKA3N7rbzgJEW4EEX4G5FW69GYLGAI1O3xDMNHopQGl8x3TSZ6uI4YaIiLoHrR7QeoePlOCul0JOiz1O3jDldkjnuZ3S5nI2PHY7ve+tA1y1Z+zrmr7mcniPefeNZ+oW3d7eq47/pDbPBW2NBR4/0PEvbCeGGyIiIjlo9dIGFe7M9Xik4FRf2zTwuByN9g5vmPLuXXXe4OUtKPffeXdGQbmn3hvI6r2PXc2Pqbx4LcMNERFRsNFoAI1Z9ZChFs4dTUREREGF4YaIiIiCCsMNERERBRWGGyIiIgoqDDdEREQUVBhuiIiIKKgw3BAREVFQYbghIiKioMJwQ0REREGF4YaIiIiCCsMNERERBRWGGyIiIgoqDDdEREQUVBhuiIiIKKjo1G6A0kRRBADYbDaVW0JERERt5fu77fs73ppuF26qqqoAAMnJySq3hIiIiM5XVVUVwsPDWz1HENsSgYKIx+PByZMnERoaCkEQAvrZNpsNycnJKCgoQFhYWEA/m5rj9VYWr7eyeL2VxeutrPZcb1EUUVVVhcTERGg0rVfVdLueG41Gg549e8r6HWFhYfwfh4J4vZXF660sXm9l8Xor63yv97l6bHxYUExERERBheGGiIiIggrDTQAZjUY8/fTTMBqNajelW+D1Vhavt7J4vZXF660sua93tysoJiIiouDGnhsiIiIKKgw3REREFFQYboiIiCioMNwQERFRUGG4CZAlS5YgNTUVJpMJ6enp2Lx5s9pNChqbNm3ChAkTkJiYCEEQ8OGHHzZ5XRRFzJ8/H4mJiTCbzbjyyiuxZ88edRrbxS1YsAAXX3wxQkNDERsbi4kTJ2Lfvn1NzuH1DpylS5di2LBh/onMMjIy8Pnnn/tf57WW14IFCyAIAubMmeM/xmseOPPnz4cgCE22+Ph4/+tyXmuGmwBYuXIl5syZgyeeeAK7du3CmDFjkJ2djfz8fLWbFhTsdjuGDx+OxYsXt/j6iy++iIULF2Lx4sXYtm0b4uPjcc011/jXEaO227hxI2bOnImtW7di3bp1cLlcyMrKgt1u95/D6x04PXv2xAsvvIDt27dj+/btuPrqq3HTTTf5/wPPay2fbdu24Y033sCwYcOaHOc1D6zBgwejsLDQv+Xm5vpfk/Vai9Rhl1xyiThjxowmxwYOHCj+8Y9/VKlFwQuAuGbNGv9zj8cjxsfHiy+88IL/WF1dnRgeHi6+9tprKrQwuJSUlIgAxI0bN4qiyOuthMjISPHNN9/ktZZRVVWV2K9fP3HdunXiFVdcIT788MOiKPLf70B7+umnxeHDh7f4mtzXmj03HeR0OrFjxw5kZWU1OZ6VlYXvv/9epVZ1H0eOHEFRUVGT6280GnHFFVfw+gdAZWUlACAqKgoAr7ec3G433n//fdjtdmRkZPBay2jmzJm4/vrrMW7cuCbHec0D78CBA0hMTERqairuuOMOHD58GID817rbLZwZaKWlpXC73YiLi2tyPC4uDkVFRSq1qvvwXeOWrv+xY8fUaFLQEEURc+fOxWWXXYYhQ4YA4PWWQ25uLjIyMlBXV4eQkBCsWbMGgwYN8v8Hntc6sN5//33s3LkT27Zta/Ya//0OrFGjRuG9995D//79UVxcjOeeew6ZmZnYs2eP7Nea4SZABEFo8lwUxWbHSD68/oE3a9Ys/Pzzz/juu++avcbrHTgDBgxATk4OTp8+jQ8++ABTp07Fxo0b/a/zWgdOQUEBHn74YXz11VcwmUxnPY/XPDCys7P9j4cOHYqMjAz06dMH7777Li699FIA8l1rDkt1UExMDLRabbNempKSkmaJlALPV3nP6x9YDz30ED7++GN8++236Nmzp/84r3fgGQwG9O3bFyNHjsSCBQswfPhwvPzyy7zWMtixYwdKSkqQnp4OnU4HnU6HjRs34pVXXoFOp/NfV15zeVitVgwdOhQHDhyQ/d9vhpsOMhgMSE9Px7p165ocX7duHTIzM1VqVfeRmpqK+Pj4Jtff6XRi48aNvP7tIIoiZs2ahdWrV2P9+vVITU1t8jqvt/xEUYTD4eC1lsHYsWORm5uLnJwc/zZy5EhMnjwZOTk5uOCCC3jNZeRwOJCXl4eEhAT5//3ucEkyie+//76o1+vFt956S9y7d684Z84c0Wq1ikePHlW7aUGhqqpK3LVrl7hr1y4RgLhw4UJx165d4rFjx0RRFMUXXnhBDA8PF1evXi3m5uaKv/3tb8WEhATRZrOp3PKu54EHHhDDw8PFDRs2iIWFhf6tpqbGfw6vd+DMmzdP3LRpk3jkyBHx559/Fv/0pz+JGo1G/Oqrr0RR5LVWQuO7pUSR1zyQHn30UXHDhg3i4cOHxa1bt4o33HCDGBoa6v/bKOe1ZrgJkFdffVXs1auXaDAYxIsuush/6yx13LfffisCaLZNnTpVFEXplsKnn35ajI+PF41Go3j55ZeLubm56ja6i2rpOgMQly1b5j+H1ztw7rnnHv9/N3r06CGOHTvWH2xEkddaCWeGG17zwLn99tvFhIQEUa/Xi4mJieItt9wi7tmzx/+6nNdaEEVR7Hj/DxEREVHnwJobIiIiCioMN0RERBRUGG6IiIgoqDDcEBERUVBhuCEiIqKgwnBDREREQYXhhoiIiIIKww0REREFFYYbIuqWBEHAhx9+qHYziEgGDDdEpLhp06ZBEIRm23XXXad204goCOjUbgARdU/XXXcdli1b1uSY0WhUqTVEFEzYc0NEqjAajYiPj2+yRUZGApCGjJYuXYrs7GyYzWakpqZi1apVTd6fm5uLq6++GmazGdHR0bjvvvtQXV3d5Jy3334bgwcPhtFoREJCAmbNmtXk9dLSUtx8882wWCzo168fPv74Y/9rFRUVmDx5Mnr06AGz2Yx+/fo1C2NE1Dkx3BBRp/TnP/8ZkyZNwu7du/G73/0Ov/3tb5GXlwcAqKmpwXXXXYfIyEhs27YNq1atwtdff90kvCxduhQzZ87Efffdh9zcXHz88cfo27dvk+945plncNttt+Hnn3/G+PHjMXnyZJSXl/u/f+/evfj888+Rl5eHpUuXIiYmRrkLQETtF5C1xYmIzsPUqVNFrVYrWq3WJtuzzz4riqIoAhBnzJjR5D2jRo0SH3jgAVEURfGNN94QIyMjxerqav/rn332majRaMSioiJRFEUxMTFRfOKJJ87aBgDik08+6X9eXV0tCoIgfv7556IoiuKECRPEu+++OzA/mIgUxZobIlLFVVddhaVLlzY5FhUV5X+ckZHR5LWMjAzk5OQAAPLy8jB8+HBYrVb/66NHj4bH48G+ffsgCAJOnjyJsWPHttqGYcOG+R9brVaEhoaipKQEAPDAAw9g0qRJ2LlzJ7KysjBx4kRkZma267cSkbIYbohIFVartdkw0bkIggAAEEXR/7ilc8xmc5s+T6/XN3uvx+MBAGRnZ+PYsWP47LPP8PXXX2Ps2LGYOXMm/va3v51Xm4lIeay5IaJOaevWrc2eDxw4EAAwaNAg5OTkwG63+1/fsmULNBoN+vfvj9DQUPTu3RvffPNNh9rQo0cPTJs2Df/+97+xaNEivPHGGx36PCJSBntuiEgVDocDRUVFTY7pdDp/0e6qVaswcuRIXHbZZVi+fDl++uknvPXWWwCAyZMn4+mnn8bUqVMxf/58nDp1Cg899BDuuusuxMXFAQDmz5+PGTNmIDY2FtnZ2aiqqsKWLVvw0EMPtal9Tz31FNLT0zF48GA4HA58+umnSEtLC+AVICK5MNwQkSq++OILJCQkNDk2YMAA/PrrrwCkO5nef/99PPjgg4iPj8fy5csxaNAgAIDFYsGXX36Jhx9+GBdffDEsFgsmTZqEhQsX+j9r6tSpqKurw0svvYTHHnsMMTEx+M1vftPm9hkMBsybNw9Hjx6F2WzGmDFj8P777wfglxOR3ARRFEW1G0FE1JggCFizZg0mTpyodlOIqAtizQ0REREFFYYbIiIiCiqsuSGiToej5UTUEey5ISIioqDCcENERERBheGGiIiIggrDDREREQUVhhsiIiIKKgw3REREFFQYboiIiCioMNwQERFRUPn/VVtV/WK7cNEAAAAASUVORK5CYII=", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot losses \n", + "losses = history.history['loss']\n", + "val_losses = history.history['val_loss']\n", + "\n", + "plt.plot(losses)\n", + "plt.plot(val_losses)\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('loss')\n", + "\n", + "plt.legend(['loss','val_loss'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ef70d735-63a6-4b11-b659-263f9030acd7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0991\n", + "test loss 0.09906331449747086\n", + "\u001b[1m313/313\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Evaluation on test data \n", + "test_loss = mymodel.evaluate(X_test,y_test)\n", + "print(\"test loss\",test_loss)\n", + "\n", + "test_labels_p = mymodel.predict(X_test)\n", + "test_labels_p = np.argmax(test_labels_p, axis=1 )\n", + "\n", + "n = 0\n", + "\n", + "f,axs = plt.subplots(1,10,figsize = (15,15))\n", + "for i in range(len(test_labels)): \n", + " if n >= 10 :\n", + " break\n", + " if (test_labels_p[i] != test_labels[i]):\n", + " axs[n].imshow(test_images[i],cmap='gray')\n", + " axs[n].set_title(f'{test_labels[i]} -> {test_labels_p[i]}')\n", + " axs[n].axis('off')\n", + " n= n+1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79b41f2d-d6f6-4ede-8af0-5a4e600157c3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/mnist-mlp-regularization/mnist-mlp-pred.ipynb b/mnist-mlp-regularization/mnist-mlp-pred.ipynb new file mode 100644 index 0000000..6b3029b --- /dev/null +++ b/mnist-mlp-regularization/mnist-mlp-pred.ipynb @@ -0,0 +1,436 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "id": "fe7570b6-1e0d-480f-b6cc-17868ec7ea43", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import datasets,utils,models,layers,optimizers,losses\n", + "import numpy as np \n", + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "markdown", + "id": "bf9f047c-ffaf-4cf8-973f-21b8f0bcba83", + "metadata": {}, + "source": [ + "# data preparation " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "91befa7a-0369-41c5-a042-e0256b97b76a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "(train_images , train_labels), (test_images,test_labels ) = datasets.mnist.load_data()\n", + "\n", + "# preprocessing\n", + "X_train = train_images.reshape(60000,784)\n", + "X_test = test_images.reshape(10000,784)\n", + "\n", + "# Normalizing (0,1 )\n", + "X_train = X_train.astype('float') / 255\n", + "X_test = X_test.astype('float') / 255\n", + "\n", + "y_train = utils.to_categorical(train_labels)\n", + "y_test = utils.to_categorical(test_labels) \n", + "\n", + "print(train_labels[0])\n", + "print(y_train[0])\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3c80e2f2-1805-4dcb-918d-f7ab72f95a38", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#visualize Sample Traning Data \n", + "for i in range(5):\n", + " plt.subplot(1,5,i+1)\n", + " plt.imshow(train_images[i],cmap='gray')\n", + " plt.title(train_labels[i])\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "markdown", + "id": "5e6329a7-76c8-4851-944d-63c83046a8cc", + "metadata": {}, + "source": [ + "# model creation" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d538ccae-1104-4be7-ae82-abff974847d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    Model: \"sequential_1\"\n",
+       "
    \n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_1\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
    โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+       "โ”ƒ Layer (type)                         โ”ƒ Output Shape                โ”ƒ         Param # โ”ƒ\n",
+       "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+       "โ”‚ dense_3 (Dense)                      โ”‚ (None, 500)                 โ”‚         392,500 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_4 (Dense)                      โ”‚ (None, 100)                 โ”‚          50,100 โ”‚\n",
+       "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+       "โ”‚ dense_5 (Dense)                      โ”‚ (None, 10)                  โ”‚           1,010 โ”‚\n",
+       "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
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    \n" + ], + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ dense_3 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m) โ”‚ \u001b[38;5;34m392,500\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_4 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) โ”‚ \u001b[38;5;34m50,100\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_5 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m1,010\u001b[0m โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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\u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.3022 - val_loss: 0.2872\n", + "Epoch 47/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.2999 - val_loss: 0.2854\n", + "Epoch 48/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.2977 - val_loss: 0.2835\n", + "Epoch 49/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.2955 - val_loss: 0.2819\n", + "Epoch 50/50\n", + "\u001b[1m375/375\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.2935 - val_loss: 0.2800\n" + ] + } + ], + "source": [ + "history = mymodel.fit(X_train,y_train,batch_size=128,epochs=50,validation_split=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5faabfcd-bd9c-4db2-870e-ac838ff72265", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot losses \n", + "losses = history.history['loss']\n", + "val_losses = history.history['val_loss']\n", + "\n", + "plt.plot(losses)\n", + "plt.plot(val_losses)\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('loss')\n", + "\n", + "plt.legend(['loss','val_loss'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ef70d735-63a6-4b11-b659-263f9030acd7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2786\n", + "test loss 0.27857887744903564\n", + "\u001b[1m313/313\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Evaluation on test data \n", + "test_loss = mymodel.evaluate(X_test,y_test)\n", + "print(\"test loss\",test_loss)\n", + "\n", + "test_labels_p = mymodel.predict(X_test)\n", + "test_labels_p = np.argmax(test_labels_p, axis=1 )\n", + "\n", + "n = 0\n", + "\n", + "f,axs = plt.subplots(1,10,figsize = (15,15))\n", + "for i in range(len(test_labels)): \n", + " if n >= 10 :\n", + " break\n", + " if (test_labels_p[i] != test_labels[i]):\n", + " axs[n].imshow(test_images[i],cmap='gray')\n", + " axs[n].set_title(f'{test_labels[i]} -> {test_labels_p[i]}')\n", + " axs[n].axis('off')\n", + " n= n+1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79b41f2d-d6f6-4ede-8af0-5a4e600157c3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}