{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-09-18T01:41:40.736760Z","iopub.execute_input":"2023-09-18T01:41:40.737157Z","iopub.status.idle":"2023-09-18T01:41:41.115315Z","shell.execute_reply.started":"2023-09-18T01:41:40.737121Z","shell.execute_reply":"2023-09-18T01:41:41.112755Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nimport matplotlib.pyplot as plt","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-09-18T01:41:41.117509Z","iopub.execute_input":"2023-09-18T01:41:41.118166Z","iopub.status.idle":"2023-09-18T01:41:49.554775Z","shell.execute_reply.started":"2023-09-18T01:41:41.118127Z","shell.execute_reply":"2023-09-18T01:41:49.553559Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div style=\"font-family: Helvetica; text-align: center; font-size:17px;\">\n    <h2 style=\"color: white;\n           background-color: #00a1f1;\n           font-family: Helvetica;\n           border-radius: 5px;\n           letter-spacing: 0.5px;\n           text-align: center;\n           padding: 10px;\">Load, split data and Create callbacks for Model</h2>\n</div>","metadata":{}},{"cell_type":"code","source":"X = pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")\ny = X['label']\nX = X.drop('label', axis=1)\n\nX = X.to_numpy().reshape((-1, 28, 28, 1))\nX = X.astype(\"float64\") / 255\ny = tf.one_hot(y, depth=10).numpy()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-09-18T01:41:49.556338Z","iopub.execute_input":"2023-09-18T01:41:49.557208Z","iopub.status.idle":"2023-09-18T01:41:57.231162Z","shell.execute_reply.started":"2023-09-18T01:41:49.557174Z","shell.execute_reply":"2023-09-18T01:41:57.229947Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=444)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-09-18T01:41:57.233938Z","iopub.execute_input":"2023-09-18T01:41:57.234295Z","iopub.status.idle":"2023-09-18T01:41:57.971708Z","shell.execute_reply.started":"2023-09-18T01:41:57.234263Z","shell.execute_reply":"2023-09-18T01:41:57.970569Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# class myCallback(tf.keras.callbacks.Callback):\n#     def on_epoch_end(self, epoch, logs={}):\n#         if(logs.get('accuracy')>0.995):\n#             print(\"\\nReached 99.5% accuracy so cancelling training!\")\n#             self.model.stop_training = True\n\n# callbacks = myCallback()\n\nfrom tensorflow.keras.callbacks import EarlyStopping\n\n# Define the early stopping callback\nearly_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-09-18T01:41:57.973115Z","iopub.execute_input":"2023-09-18T01:41:57.973488Z","iopub.status.idle":"2023-09-18T01:41:57.981373Z","shell.execute_reply.started":"2023-09-18T01:41:57.973454Z","shell.execute_reply":"2023-09-18T01:41:57.980250Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div style=\"font-family: Helvetica; text-align: center; font-size:17px;\">\n    <h2 style=\"color: white;\n           background-color: #00a1f1;\n           font-family: Helvetica;\n           border-radius: 5px;\n           letter-spacing: 0.5px;\n           text-align: center;\n           padding: 10px;\">Model</h2>\n</div>","metadata":{}},{"cell_type":"code","source":"def plot_learning_curve(history):\n    import pandas as pd\n    pd.DataFrame(history.history).plot(figsize=(8,5))\n    plt.grid(True)\n    plt.gca().set_ylim(0,1)\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:41:57.982866Z","iopub.execute_input":"2023-09-18T01:41:57.983207Z","iopub.status.idle":"2023-09-18T01:41:57.991649Z","shell.execute_reply.started":"2023-09-18T01:41:57.983175Z","shell.execute_reply":"2023-09-18T01:41:57.990624Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Baseline model\n![image.png](attachment:f819fbb0-8bf2-4c89-b02f-d29757fa5de9.png)","metadata":{},"attachments":{"f819fbb0-8bf2-4c89-b02f-d29757fa5de9.png":{"image/png":"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"}}},{"cell_type":"code","source":"base_model = keras.Sequential([\n    keras.layers.Flatten(input_shape=[28,28]),\n    keras.layers.Dense(300, activation=\"relu\"),\n    keras.layers.Dense(100, activation=\"relu\"),\n    keras.layers.Dense(10, activation=\"softmax\")\n])\n\nbase_model.compile(loss=\"categorical_crossentropy\", optimizer=\"sgd\", metrics=[\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-09-18T00:59:34.765200Z","iopub.execute_input":"2023-09-18T00:59:34.765611Z","iopub.status.idle":"2023-09-18T00:59:34.884281Z","shell.execute_reply.started":"2023-09-18T00:59:34.765577Z","shell.execute_reply":"2023-09-18T00:59:34.883233Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## If you replace this block multiple times, you can see the `val_accuracy` increase. \n## The reason is that the weights in the model were saved. \n## This is equivalent to setting `epochs = 100`, `1000`, etc. \n## So if you re-run the above code and run this block of code, you can see it will return to normal.\nhistory = base_model.fit(X_train, y_train,\n                    batch_size=64,\n                    epochs=10,\n                    validation_split=0.1,\n                    callbacks=[early_stopping])","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2023-09-18T00:59:50.890918Z","iopub.execute_input":"2023-09-18T00:59:50.891607Z","iopub.status.idle":"2023-09-18T01:00:32.773930Z","shell.execute_reply.started":"2023-09-18T00:59:50.891574Z","shell.execute_reply":"2023-09-18T01:00:32.772707Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"base_model.evaluate(X_test, y_test)","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:00:44.618301Z","iopub.execute_input":"2023-09-18T01:00:44.618711Z","iopub.status.idle":"2023-09-18T01:00:45.448941Z","shell.execute_reply.started":"2023-09-18T01:00:44.618680Z","shell.execute_reply":"2023-09-18T01:00:45.447822Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_learning_curve(history)","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:00:50.114530Z","iopub.execute_input":"2023-09-18T01:00:50.114944Z","iopub.status.idle":"2023-09-18T01:00:50.661840Z","shell.execute_reply.started":"2023-09-18T01:00:50.114881Z","shell.execute_reply":"2023-09-18T01:00:50.660844Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Optimizer model\n1. **Epoch (Number of Training Iterations)**: I adjusted the number of training iterations to optimize model training.\n2. **Activation Function**: I explored different activation functions to enhance the model's ability to capture complex patterns in the data.\n3. **Kernel Initializer**: I tested various kernel initialization methods to improve how the model initializes its weights, potentially affecting convergence.\n4. **Batch Normalization**: I examined the effect of Batch Normalization on stabilizing training and improving generalization by normalizing the activations in each layer.\n5. etc...\n\nI also recommend this book: https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/\n![image.png](attachment:d7bfa303-9d82-4de1-8baa-45f0ae88603a.png)","metadata":{},"attachments":{"d7bfa303-9d82-4de1-8baa-45f0ae88603a.png":{"image/png":"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"}}},{"cell_type":"code","source":"model = keras.Sequential([\n    keras.layers.Flatten(input_shape=[28,28]),\n    keras.layers.Dense(300, kernel_initializer=\"he_normal\", use_bias=False),\n    keras.layers.BatchNormalization(),\n    keras.layers.Activation(\"relu\"),\n    keras.layers.Dense(100, kernel_initializer=\"he_normal\", use_bias=False),\n    keras.layers.BatchNormalization(),\n    keras.layers.Activation(\"relu\"),\n    keras.layers.Dense(10, activation=\"softmax\",)\n])\n#optimizer = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999)\nmodel.compile(loss=\"categorical_crossentropy\", optimizer='adam', metrics=[\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:00:54.112031Z","iopub.execute_input":"2023-09-18T01:00:54.112433Z","iopub.status.idle":"2023-09-18T01:00:54.207149Z","shell.execute_reply.started":"2023-09-18T01:00:54.112401Z","shell.execute_reply":"2023-09-18T01:00:54.205314Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nhistory = model.fit(X_train, y_train,\n                    batch_size=64,\n                    epochs=30,\n                    validation_split=0.1,\n                    callbacks=[early_stopping])","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2023-09-18T01:00:59.807493Z","iopub.execute_input":"2023-09-18T01:00:59.808617Z","iopub.status.idle":"2023-09-18T01:01:36.374963Z","shell.execute_reply.started":"2023-09-18T01:00:59.808568Z","shell.execute_reply":"2023-09-18T01:01:36.373917Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.evaluate(X_test, y_test)","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:02:00.108029Z","iopub.execute_input":"2023-09-18T01:02:00.108433Z","iopub.status.idle":"2023-09-18T01:02:00.988939Z","shell.execute_reply.started":"2023-09-18T01:02:00.108402Z","shell.execute_reply":"2023-09-18T01:02:00.987844Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_learning_curve(history)","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:02:07.627660Z","iopub.execute_input":"2023-09-18T01:02:07.628098Z","iopub.status.idle":"2023-09-18T01:02:07.962951Z","shell.execute_reply.started":"2023-09-18T01:02:07.628065Z","shell.execute_reply":"2023-09-18T01:02:07.961916Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div style=\"font-family: Helvetica; text-align: center; font-size:17px;\">\n    <h2 style=\"color: white;\n           background-color: #00a1f1;\n           font-family: Helvetica;\n           border-radius: 5px;\n           letter-spacing: 0.5px;\n           text-align: center;\n           padding: 10px;\">CNN Model</h2>\n</div>","metadata":{}},{"cell_type":"markdown","source":"## CNN without data augmentation","metadata":{}},{"cell_type":"code","source":"cnn_model = keras.models.Sequential([\n keras.layers.Conv2D(64, 7, activation=\"relu\", padding=\"same\",\n input_shape=[28, 28, 1]),\n keras.layers.MaxPooling2D(2),\n keras.layers.Conv2D(128, 3, activation=\"relu\", padding=\"same\"),\n keras.layers.Conv2D(128, 3, activation=\"relu\", padding=\"same\"),\n keras.layers.MaxPooling2D(2),\n keras.layers.Conv2D(256, 3, activation=\"relu\", padding=\"same\"),\n keras.layers.Conv2D(256, 3, activation=\"relu\", padding=\"same\"),\n keras.layers.MaxPooling2D(2),\n keras.layers.Flatten(),\n keras.layers.Dense(128, activation=\"relu\"),\n keras.layers.Dropout(0.5),\n keras.layers.Dense(64, activation=\"relu\"),\n keras.layers.Dropout(0.5),\n keras.layers.Dense(10, activation=\"softmax\")\n])\ncnn_model.compile(loss=\"categorical_crossentropy\", optimizer='adam', metrics=[\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:41:57.994207Z","iopub.execute_input":"2023-09-18T01:41:57.995024Z","iopub.status.idle":"2023-09-18T01:41:58.190056Z","shell.execute_reply.started":"2023-09-18T01:41:57.994989Z","shell.execute_reply":"2023-09-18T01:41:58.188928Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nhistory = cnn_model.fit(X_train, y_train,\n                    batch_size=64,\n                    epochs=30,\n                    validation_split=0.1,\n                    callbacks=[early_stopping])","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2023-09-18T01:13:47.816128Z","iopub.execute_input":"2023-09-18T01:13:47.816519Z","iopub.status.idle":"2023-09-18T01:14:47.667536Z","shell.execute_reply.started":"2023-09-18T01:13:47.816489Z","shell.execute_reply":"2023-09-18T01:14:47.666271Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_learning_curve(history)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cnn_model.evaluate(X_test, y_test)","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:15:01.677535Z","iopub.execute_input":"2023-09-18T01:15:01.677957Z","iopub.status.idle":"2023-09-18T01:15:04.358902Z","shell.execute_reply.started":"2023-09-18T01:15:01.677912Z","shell.execute_reply":"2023-09-18T01:15:04.357936Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## CNN with data augmentation","metadata":{}},{"cell_type":"code","source":"datagen = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=10, # Rotating randomly the images up to 25°\n                             width_shift_range=0.05, # Moving the images from left to right\n                             height_shift_range=0.05, # Then from top to bottom\n                             shear_range=0.10, \n                             zoom_range=0.05, # Zooming randomly up to 20%\n                             zca_whitening=False,\n                             horizontal_flip=False, \n                             vertical_flip=False,\n                            fill_mode = 'nearest')","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:41:58.191613Z","iopub.execute_input":"2023-09-18T01:41:58.191981Z","iopub.status.idle":"2023-09-18T01:41:58.198487Z","shell.execute_reply.started":"2023-09-18T01:41:58.191948Z","shell.execute_reply":"2023-09-18T01:41:58.197030Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"datagen.fit(X_train)\n\nbatch_size = 32\ntrain_data_generator = datagen.flow(X_train, y_train, batch_size=batch_size)","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:41:58.200145Z","iopub.execute_input":"2023-09-18T01:41:58.200914Z","iopub.status.idle":"2023-09-18T01:41:58.336847Z","shell.execute_reply.started":"2023-09-18T01:41:58.200880Z","shell.execute_reply":"2023-09-18T01:41:58.335747Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from keras.callbacks import LearningRateScheduler\ndef lr_schedule(epoch):\n    initial_lr = 0.001  \n    if epoch < 5:\n        return initial_lr \n    else:\n        return initial_lr * 0.1 \n\nlr_scheduler = LearningRateScheduler(lr_schedule)","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:42:15.666228Z","iopub.execute_input":"2023-09-18T01:42:15.666628Z","iopub.status.idle":"2023-09-18T01:42:15.673831Z","shell.execute_reply.started":"2023-09-18T01:42:15.666597Z","shell.execute_reply":"2023-09-18T01:42:15.671835Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\noptimizer = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999)\ncnn_model.compile(loss=\"categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])\nhistory = cnn_model.fit(train_data_generator,\n                    batch_size=64,\n                    epochs=30,\n                    callbacks=[early_stopping, lr_scheduler])","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-09-18T01:42:24.261379Z","iopub.execute_input":"2023-09-18T01:42:24.261759Z","iopub.status.idle":"2023-09-18T01:51:23.408192Z","shell.execute_reply.started":"2023-09-18T01:42:24.261728Z","shell.execute_reply":"2023-09-18T01:51:23.407241Z"},"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_learning_curve(history)","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:56:42.860894Z","iopub.execute_input":"2023-09-18T01:56:42.861313Z","iopub.status.idle":"2023-09-18T01:56:43.167180Z","shell.execute_reply.started":"2023-09-18T01:56:42.861283Z","shell.execute_reply":"2023-09-18T01:56:43.166245Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cnn_model.evaluate(X_test, y_test)","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:52:00.411368Z","iopub.execute_input":"2023-09-18T01:52:00.411866Z","iopub.status.idle":"2023-09-18T01:52:02.126868Z","shell.execute_reply.started":"2023-09-18T01:52:00.411829Z","shell.execute_reply":"2023-09-18T01:52:02.125910Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div style=\"font-family: Helvetica; text-align: center; font-size:17px;\">\n    <h2 style=\"color: white;\n           background-color: #00a1f1;\n           font-family: Helvetica;\n           border-radius: 5px;\n           letter-spacing: 0.5px;\n           text-align: center;\n           padding: 10px;\">Predict and Submit result</h2>\n</div>","metadata":{}},{"cell_type":"code","source":"test = pd.read_csv(\"/kaggle/input/digit-recognizer/test.csv\")\nx_test = test.to_numpy().reshape((-1, 28, 28, 1))\nx_test = x_test.astype(\"float64\") / 255","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-09-18T01:52:09.673858Z","iopub.execute_input":"2023-09-18T01:52:09.674293Z","iopub.status.idle":"2023-09-18T01:52:13.238814Z","shell.execute_reply.started":"2023-09-18T01:52:09.674251Z","shell.execute_reply":"2023-09-18T01:52:13.237616Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def export_result(model, X_test):\n    y_pred = model.predict(X_test)\n    labels = []\n    ids = []\n    for i in range(len(y_pred)):\n        ids.append(i + 1)\n        labels.append(np.argmax(y_pred[i]))\n    ans_df = pd.DataFrame({\"ImageId\": ids, \"Label\": labels})\n    return ans_df","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-09-18T01:52:13.964390Z","iopub.execute_input":"2023-09-18T01:52:13.965355Z","iopub.status.idle":"2023-09-18T01:52:13.972514Z","shell.execute_reply.started":"2023-09-18T01:52:13.965319Z","shell.execute_reply":"2023-09-18T01:52:13.971225Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result_df = export_result(cnn_model, x_test)\nresult_df.to_csv(\"/kaggle/working/submission.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2023-09-18T01:52:18.389935Z","iopub.execute_input":"2023-09-18T01:52:18.390298Z","iopub.status.idle":"2023-09-18T01:52:21.624034Z","shell.execute_reply.started":"2023-09-18T01:52:18.390262Z","shell.execute_reply":"2023-09-18T01:52:21.623056Z"},"trusted":true},"execution_count":null,"outputs":[]}]}