Logistic_regression_multi_class_with_fashion_mnist.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"5NDNcqKFm8QT"},"source":["from sklearn.linear_model import LogisticRegression"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XKv6pUV1m9sg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1676152146823,"user_tz":-120,"elapsed":6059,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}},"outputId":"98327c3a-9a27-446d-a870-08fce1a145dd"},"source":["from keras.datasets import fashion_mnist\n","(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n","29515/29515 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n","26421880/26421880 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n","5148/5148 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n","4422102/4422102 [==============================] - 0s 0us/step\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G8JKbHbboBxK","outputId":"f22d2ed0-55c1-4b3c-ef48-c5f97ddf9382","executionInfo":{"status":"ok","timestamp":1676152146827,"user_tz":-120,"elapsed":125,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}}},"source":["x_train.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(60000, 28, 28)"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["y_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2WKm6Gi9LAqN","outputId":"b5100217-d092-464e-dcd1-7e942952f3d2","executionInfo":{"status":"ok","timestamp":1676152146830,"user_tz":-120,"elapsed":111,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(10000,)"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kbLwTYk5oB0H","outputId":"97cfc8c1-74a5-4d4d-d972-0e354b168f92","executionInfo":{"status":"ok","timestamp":1676152146833,"user_tz":-120,"elapsed":95,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}}},"source":["y_train[0:10]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([9, 0, 0, 3, 0, 2, 7, 2, 5, 5], dtype=uint8)"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RJNswhHkoB89","outputId":"148e05a3-99fd-4ccb-8fd0-6f39122f2965","executionInfo":{"status":"ok","timestamp":1676152146836,"user_tz":-120,"elapsed":81,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}}},"source":["#we need to flatten data into two dimensions only:  (28 * 28 = 784).\n","\n","x_train_final = x_train.reshape(-1, 784)\n","x_train_final.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(60000, 784)"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"kqk5lrgkovJI","colab":{"base_uri":"https://localhost:8080/"},"outputId":"e9002b64-a0aa-4b15-e6a8-b40101521a82","executionInfo":{"status":"ok","timestamp":1676152146839,"user_tz":-120,"elapsed":69,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}}},"source":["x_test_final = x_test.reshape(-1, 784)\n","x_test_final.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(10000, 784)"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"VqgGpBjAovL3"},"source":["#The last element of data preprocessing is their normalization.\n","\n","x_train_final = x_train_final / 255\n","x_test_final = x_test_final / 255"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_oPxy8UBpmJ2"},"source":["model=LogisticRegression()\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sTTGaqADpmMt","outputId":"8b680db7-c1d1-4874-a321-6e547b234f54","executionInfo":{"status":"ok","timestamp":1676152185982,"user_tz":-120,"elapsed":38825,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}}},"source":["model.fit(x_train_final,y_train)  "],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"output_type":"execute_result","data":{"text/plain":["LogisticRegression()"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"R42mRw_MpmUY"},"source":["y_pred= model.predict(x_test_final)"],"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_pred"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"60xhEE3u8K3K","outputId":"92db8d2c-3f88-4c03-a554-9e10f8bc0c56","executionInfo":{"status":"ok","timestamp":1676152185989,"user_tz":-120,"elapsed":177,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([9, 2, 1, ..., 8, 1, 5], dtype=uint8)"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score\n","accuracy_score(y_test,y_pred)*100"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"h9ximKMBCst1","outputId":"1b21168c-a9d6-4e89-a027-2d4eab5a49d9","executionInfo":{"status":"ok","timestamp":1676152185992,"user_tz":-120,"elapsed":146,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["84.39"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt  \n","plt.imshow(x_test[1])\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"JoD9gnHL-QsW","executionInfo":{"status":"ok","timestamp":1676152186354,"user_tz":-120,"elapsed":490,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}},"outputId":"75c6cc3a-30a6-4a04-a64b-4f3e805d71a0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f9a04f6c640>"]},"metadata":{},"execution_count":14},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}