handwritten-digit-classification-using-ann.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"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","execution":{"iopub.status.busy":"2023-08-07T15:42:25.958738Z","iopub.execute_input":"2023-08-07T15:42:25.959172Z","iopub.status.idle":"2023-08-07T15:42:25.995827Z","shell.execute_reply.started":"2023-08-07T15:42:25.959135Z","shell.execute_reply":"2023-08-07T15:42:25.994792Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import tensorflow\nfrom tensorflow import keras\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Dense, Flatten","metadata":{"execution":{"iopub.status.busy":"2023-08-07T15:44:59.107112Z","iopub.execute_input":"2023-08-07T15:44:59.107604Z","iopub.status.idle":"2023-08-07T15:44:59.114165Z","shell.execute_reply.started":"2023-08-07T15:44:59.107565Z","shell.execute_reply":"2023-08-07T15:44:59.112616Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()","metadata":{"execution":{"iopub.status.busy":"2023-08-07T15:46:11.842195Z","iopub.execute_input":"2023-08-07T15:46:11.842608Z","iopub.status.idle":"2023-08-07T15:46:12.330548Z","shell.execute_reply.started":"2023-08-07T15:46:11.842578Z","shell.execute_reply":"2023-08-07T15:46:12.329376Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n11490434/11490434 [==============================] - 0s 0us/step\n","output_type":"stream"}]},{"cell_type":"code","source":"X_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-08-07T15:46:26.108424Z","iopub.execute_input":"2023-08-07T15:46:26.108827Z","iopub.status.idle":"2023-08-07T15:46:26.117259Z","shell.execute_reply.started":"2023-08-07T15:46:26.108786Z","shell.execute_reply":"2023-08-07T15:46:26.116125Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"(60000, 28, 28)"},"metadata":{}}]},{"cell_type":"code","source":"y_train","metadata":{"execution":{"iopub.status.busy":"2023-08-07T15:47:16.541317Z","iopub.execute_input":"2023-08-07T15:47:16.541818Z","iopub.status.idle":"2023-08-07T15:47:16.551497Z","shell.execute_reply.started":"2023-08-07T15:47:16.541780Z","shell.execute_reply":"2023-08-07T15:47:16.550010Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"},"metadata":{}}]},{"cell_type":"code","source":"# plot first image in X_train\nimport matplotlib.pyplot as plt\nplt.imshow(X_train[0])","metadata":{"execution":{"iopub.status.busy":"2023-08-07T15:47:43.313992Z","iopub.execute_input":"2023-08-07T15:47:43.314364Z","iopub.status.idle":"2023-08-07T15:47:43.646660Z","shell.execute_reply.started":"2023-08-07T15:47:43.314333Z","shell.execute_reply":"2023-08-07T15:47:43.645481Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7fb3a30c7220>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"X_train 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activation='relu'))\nmodel.add(Dense(10, activation='softmax'))","metadata":{"execution":{"iopub.status.busy":"2023-08-07T15:50:35.871635Z","iopub.execute_input":"2023-08-07T15:50:35.872153Z","iopub.status.idle":"2023-08-07T15:50:35.982302Z","shell.execute_reply.started":"2023-08-07T15:50:35.872110Z","shell.execute_reply":"2023-08-07T15:50:35.981181Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":"**Flatten:** Reshaping the (1, 28, 28) tensor into a 1D vector of length 28*28 = 784. This would be suitable for feeding the data into a fully connected (dense) layer in the neural network.","metadata":{}},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2023-08-07T15:50:48.361526Z","iopub.execute_input":"2023-08-07T15:50:48.361938Z","iopub.status.idle":"2023-08-07T15:50:48.384178Z","shell.execute_reply.started":"2023-08-07T15:50:48.361904Z","shell.execute_reply":"2023-08-07T15:50:48.382725Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Model: \"sequential_1\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n flatten (Flatten)           (None, 784)               0         \n                                                                 \n dense (Dense)               (None, 128)               100480    \n                                                                 \n dense_1 (Dense)             (None, 32)                4128      \n                                                                 \n dense_2 (Dense)             (None, 10)                330       \n                                                                 \n=================================================================\nTotal params: 104,938\nTrainable params: 104,938\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2023-08-07T15:51:33.117893Z","iopub.execute_input":"2023-08-07T15:51:33.118325Z","iopub.status.idle":"2023-08-07T15:51:33.138049Z","shell.execute_reply.started":"2023-08-07T15:51:33.118290Z","shell.execute_reply":"2023-08-07T15:51:33.137034Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"history = model.fit(X_train, y_train, epochs=25, validation_split=0.2)","metadata":{"execution":{"iopub.status.busy":"2023-08-07T15:52:42.124557Z","iopub.execute_input":"2023-08-07T15:52:42.125402Z","iopub.status.idle":"2023-08-07T15:55:05.260066Z","shell.execute_reply.started":"2023-08-07T15:52:42.125356Z","shell.execute_reply":"2023-08-07T15:55:05.258481Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"Epoch 1/25\n1500/1500 [==============================] - 7s 4ms/step - loss: 0.2814 - accuracy: 0.9172 - val_loss: 0.1391 - val_accuracy: 0.9569\nEpoch 2/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.1171 - accuracy: 0.9637 - val_loss: 0.1012 - val_accuracy: 0.9696\nEpoch 3/25\n1500/1500 [==============================] - 5s 4ms/step - loss: 0.0817 - accuracy: 0.9747 - val_loss: 0.1033 - val_accuracy: 0.9697\nEpoch 4/25\n1500/1500 [==============================] - 6s 4ms/step - loss: 0.0604 - accuracy: 0.9812 - val_loss: 0.1038 - val_accuracy: 0.9706\nEpoch 5/25\n1500/1500 [==============================] - 5s 4ms/step - loss: 0.0493 - accuracy: 0.9844 - val_loss: 0.1058 - val_accuracy: 0.9708\nEpoch 6/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0381 - accuracy: 0.9877 - val_loss: 0.0931 - val_accuracy: 0.9743\nEpoch 7/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0302 - accuracy: 0.9903 - val_loss: 0.1112 - val_accuracy: 0.9712\nEpoch 8/25\n1500/1500 [==============================] - 5s 4ms/step - loss: 0.0257 - accuracy: 0.9915 - val_loss: 0.0925 - val_accuracy: 0.9765\nEpoch 9/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0222 - accuracy: 0.9928 - val_loss: 0.1194 - val_accuracy: 0.9721\nEpoch 10/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0186 - accuracy: 0.9937 - val_loss: 0.1141 - val_accuracy: 0.9727\nEpoch 11/25\n1500/1500 [==============================] - 5s 4ms/step - loss: 0.0161 - accuracy: 0.9948 - val_loss: 0.1173 - val_accuracy: 0.9745\nEpoch 12/25\n1500/1500 [==============================] - 6s 4ms/step - loss: 0.0178 - accuracy: 0.9940 - val_loss: 0.1240 - val_accuracy: 0.9734\nEpoch 13/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0141 - accuracy: 0.9953 - val_loss: 0.1188 - val_accuracy: 0.9760\nEpoch 14/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0106 - accuracy: 0.9965 - val_loss: 0.1356 - val_accuracy: 0.9735\nEpoch 15/25\n1500/1500 [==============================] - 6s 4ms/step - loss: 0.0131 - accuracy: 0.9956 - val_loss: 0.1453 - val_accuracy: 0.9744\nEpoch 16/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0102 - accuracy: 0.9968 - val_loss: 0.1619 - val_accuracy: 0.9711\nEpoch 17/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0112 - accuracy: 0.9961 - val_loss: 0.1399 - val_accuracy: 0.9773\nEpoch 18/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0121 - accuracy: 0.9958 - val_loss: 0.1503 - val_accuracy: 0.9748\nEpoch 19/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0111 - accuracy: 0.9963 - val_loss: 0.1403 - val_accuracy: 0.9758\nEpoch 20/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0084 - accuracy: 0.9972 - val_loss: 0.1692 - val_accuracy: 0.9719\nEpoch 21/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0082 - accuracy: 0.9971 - val_loss: 0.1593 - val_accuracy: 0.9764\nEpoch 22/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0077 - accuracy: 0.9973 - val_loss: 0.1547 - val_accuracy: 0.9742\nEpoch 23/25\n1500/1500 [==============================] - 5s 4ms/step - loss: 0.0094 - accuracy: 0.9969 - val_loss: 0.1651 - val_accuracy: 0.9767\nEpoch 24/25\n1500/1500 [==============================] - 5s 4ms/step - loss: 0.0088 - accuracy: 0.9971 - val_loss: 0.1985 - val_accuracy: 0.9728\nEpoch 25/25\n1500/1500 [==============================] - 5s 3ms/step - loss: 0.0046 - accuracy: 0.9986 - val_loss: 0.1531 - val_accuracy: 0.9785\n","output_type":"stream"}]},{"cell_type":"code","source":"y_prob = model.predict(X_test)\ny_prob","metadata":{"execution":{"iopub.status.busy":"2023-08-07T15:55:35.728916Z","iopub.execute_input":"2023-08-07T15:55:35.729406Z","iopub.status.idle":"2023-08-07T15:55:36.498251Z","shell.execute_reply.started":"2023-08-07T15:55:35.729370Z","shell.execute_reply":"2023-08-07T15:55:36.497106Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"313/313 [==============================] - 1s 2ms/step\n","output_type":"stream"},{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"array([[2.8855018e-17, 1.0363569e-18, 2.2622090e-14, ..., 9.9999994e-01,\n        1.4972198e-23, 2.3837246e-10],\n       [3.8986147e-17, 3.0206144e-09, 9.9999994e-01, ..., 2.3692060e-18,\n        2.7426833e-17, 2.6868785e-23],\n       [4.1268651e-12, 9.9999994e-01, 4.5675841e-10, ..., 1.9055292e-08,\n        8.4435996e-09, 3.1040522e-17],\n       ...,\n       [1.2868648e-22, 8.2408502e-16, 3.5469112e-26, ..., 5.7418339e-17,\n        5.8276930e-18, 4.1177013e-14],\n       [3.5919808e-26, 1.5702001e-21, 4.4906019e-31, ..., 3.8870754e-25,\n        6.7300224e-14, 3.0905702e-25],\n       [2.1877027e-15, 1.9959571e-23, 1.3118855e-22, ..., 1.3689042e-30,\n        1.1085162e-17, 4.2474657e-24]], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = y_prob.argmax(axis=1)\ny_pred","metadata":{"execution":{"iopub.status.busy":"2023-08-07T16:03:57.330070Z","iopub.execute_input":"2023-08-07T16:03:57.330513Z","iopub.status.idle":"2023-08-07T16:03:57.338251Z","shell.execute_reply.started":"2023-08-07T16:03:57.330476Z","shell.execute_reply":"2023-08-07T16:03:57.337498Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"array([7, 2, 1, ..., 4, 5, 6])"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score\naccuracy_score(y_test, y_pred)","metadata":{"execution":{"iopub.status.busy":"2023-08-07T16:00:47.589126Z","iopub.execute_input":"2023-08-07T16:00:47.589571Z","iopub.status.idle":"2023-08-07T16:00:47.600535Z","shell.execute_reply.started":"2023-08-07T16:00:47.589537Z","shell.execute_reply":"2023-08-07T16:00:47.599133Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"0.9776"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])","metadata":{"execution":{"iopub.status.busy":"2023-08-07T16:00:57.993416Z","iopub.execute_input":"2023-08-07T16:00:57.994952Z","iopub.status.idle":"2023-08-07T16:00:59.266471Z","shell.execute_reply.started":"2023-08-07T16:00:57.994889Z","shell.execute_reply":"2023-08-07T16:00:59.265139Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"[<matplotlib.lines.Line2D at 0x7fb32babace0>]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])","metadata":{"execution":{"iopub.status.busy":"2023-08-07T16:01:10.063478Z","iopub.execute_input":"2023-08-07T16:01:10.064361Z","iopub.status.idle":"2023-08-07T16:01:10.363558Z","shell.execute_reply.started":"2023-08-07T16:01:10.064318Z","shell.execute_reply":"2023-08-07T16:01:10.362278Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"[<matplotlib.lines.Line2D 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"},"metadata":{}}]},{"cell_type":"markdown","source":"These plots is indicated overfitting","metadata":{}},{"cell_type":"code","source":"plt.imshow(X_test[0])","metadata":{"execution":{"iopub.status.busy":"2023-08-07T16:06:00.360061Z","iopub.execute_input":"2023-08-07T16:06:00.360596Z","iopub.status.idle":"2023-08-07T16:06:00.662956Z","shell.execute_reply.started":"2023-08-07T16:06:00.360556Z","shell.execute_reply":"2023-08-07T16:06:00.660026Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7fb380190d60>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"model.predict(X_test[0].reshape(1,28,28)).argmax(axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-08-07T16:06:06.087479Z","iopub.execute_input":"2023-08-07T16:06:06.088166Z","iopub.status.idle":"2023-08-07T16:06:06.172961Z","shell.execute_reply.started":"2023-08-07T16:06:06.088099Z","shell.execute_reply":"2023-08-07T16:06:06.171735Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 0s 25ms/step\n","output_type":"stream"},{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"array([7])"},"metadata":{}}]}]}