TensorFlow_mnistDeep_NTI.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMDTvSBVOX3AxJ9WOtEtgR6"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"gpuClass":"standard"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"-DeP4sHeoeSQ"},"outputs":[],"source":["import numpy as np \n","import matplotlib.pyplot as plt \n","from tensorflow import keras\n","import tensorflow as tf \n","\n"]},{"cell_type":"code","source":["mnist=tf.keras.datasets.mnist\n","(x_train,y_train),(x_test,y_test)=mnist.load_data()\n","print(x_train.shape,y_train.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FQmb2UIRqBB4","executionInfo":{"status":"ok","timestamp":1675687695111,"user_tz":-120,"elapsed":485,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}},"outputId":"171e1ef0-1e98-4926-9c3b-7473cd70ee41"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(60000, 28, 28) (60000,)\n"]}]},{"cell_type":"code","source":["x_train_final=x_train.reshape((-1,784))\n","x_train_final=x_train_final/255"],"metadata":{"id":"3dR03BCqrW5w"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["x_test_final=x_test.reshape((-1,784))\n","x_test_final=x_test_final/255"],"metadata":{"id":"kuYILyX2rmUB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model=keras.Sequential()\n","model.add(keras.layers.Dense(512,input_shape=(784,),activation=\"relu\"))\n","model.add(keras.layers.Dense(256,activation=\"relu\"))\n","model.add(keras.layers.Dense(10,activation=\"softmax\"))\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZUlA9rrAsWVN","executionInfo":{"status":"ok","timestamp":1675687696979,"user_tz":-120,"elapsed":1888,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}},"outputId":"65f3a3ee-f32f-4d05-d5bb-4092eeb80488"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," dense (Dense)               (None, 512)               401920    \n","                                                                 \n"," dense_1 (Dense)             (None, 256)               131328    \n","                                                                 \n"," dense_2 (Dense)             (None, 10)                2570      \n","                                                                 \n","=================================================================\n","Total params: 535,818\n","Trainable params: 535,818\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["model.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',metrics=['accuracy'])"],"metadata":{"id":"QoHHeUNzta01"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["results=model.fit(x=x_train_final,y=y_train,shuffle=True,epochs=30,batch_size=32,validation_data=(x_test_final,y_test),verbose=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MDK6tDQ_tu55","executionInfo":{"status":"ok","timestamp":1675687900289,"user_tz":-120,"elapsed":203336,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}},"outputId":"b59aa653-baa2-49ba-8b2d-1a14241e08a2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","1875/1875 [==============================] - 11s 5ms/step - loss: 0.5676 - accuracy: 0.8558 - val_loss: 0.2969 - val_accuracy: 0.9173\n","Epoch 2/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.2756 - accuracy: 0.9214 - val_loss: 0.2371 - val_accuracy: 0.9323\n","Epoch 3/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.2239 - accuracy: 0.9362 - val_loss: 0.1985 - val_accuracy: 0.9450\n","Epoch 4/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.1894 - accuracy: 0.9463 - val_loss: 0.1727 - val_accuracy: 0.9501\n","Epoch 5/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.1638 - accuracy: 0.9533 - val_loss: 0.1570 - val_accuracy: 0.9537\n","Epoch 6/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.1439 - accuracy: 0.9592 - val_loss: 0.1393 - val_accuracy: 0.9599\n","Epoch 7/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.1281 - accuracy: 0.9638 - val_loss: 0.1281 - val_accuracy: 0.9619\n","Epoch 8/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.1152 - accuracy: 0.9674 - val_loss: 0.1164 - val_accuracy: 0.9657\n","Epoch 9/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.1044 - accuracy: 0.9705 - val_loss: 0.1108 - val_accuracy: 0.9679\n","Epoch 10/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0952 - accuracy: 0.9729 - val_loss: 0.1026 - val_accuracy: 0.9697\n","Epoch 11/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0869 - accuracy: 0.9758 - val_loss: 0.0964 - val_accuracy: 0.9718\n","Epoch 12/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0802 - accuracy: 0.9774 - val_loss: 0.0923 - val_accuracy: 0.9730\n","Epoch 13/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0735 - accuracy: 0.9795 - val_loss: 0.0921 - val_accuracy: 0.9721\n","Epoch 14/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0680 - accuracy: 0.9815 - val_loss: 0.0839 - val_accuracy: 0.9745\n","Epoch 15/30\n","1875/1875 [==============================] - 6s 3ms/step - loss: 0.0631 - accuracy: 0.9829 - val_loss: 0.0811 - val_accuracy: 0.9758\n","Epoch 16/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0587 - accuracy: 0.9843 - val_loss: 0.0818 - val_accuracy: 0.9754\n","Epoch 17/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0548 - accuracy: 0.9852 - val_loss: 0.0779 - val_accuracy: 0.9773\n","Epoch 18/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0510 - accuracy: 0.9865 - val_loss: 0.0760 - val_accuracy: 0.9763\n","Epoch 19/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0475 - accuracy: 0.9875 - val_loss: 0.0721 - val_accuracy: 0.9788\n","Epoch 20/30\n","1875/1875 [==============================] - 5s 2ms/step - loss: 0.0449 - accuracy: 0.9883 - val_loss: 0.0736 - val_accuracy: 0.9779\n","Epoch 21/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0420 - accuracy: 0.9894 - val_loss: 0.0718 - val_accuracy: 0.9786\n","Epoch 22/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0392 - accuracy: 0.9901 - val_loss: 0.0701 - val_accuracy: 0.9797\n","Epoch 23/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0370 - accuracy: 0.9907 - val_loss: 0.0697 - val_accuracy: 0.9790\n","Epoch 24/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0345 - accuracy: 0.9918 - val_loss: 0.0680 - val_accuracy: 0.9795\n","Epoch 25/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0324 - accuracy: 0.9919 - val_loss: 0.0683 - val_accuracy: 0.9794\n","Epoch 26/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0303 - accuracy: 0.9930 - val_loss: 0.0682 - val_accuracy: 0.9792\n","Epoch 27/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0287 - accuracy: 0.9934 - val_loss: 0.0669 - val_accuracy: 0.9799\n","Epoch 28/30\n","1875/1875 [==============================] - 5s 3ms/step - loss: 0.0269 - accuracy: 0.9939 - val_loss: 0.0658 - val_accuracy: 0.9798\n","Epoch 29/30\n","1875/1875 [==============================] - 6s 3ms/step - loss: 0.0252 - accuracy: 0.9947 - val_loss: 0.0642 - val_accuracy: 0.9803\n","Epoch 30/30\n","1875/1875 [==============================] - 8s 5ms/step - loss: 0.0237 - accuracy: 0.9950 - val_loss: 0.0637 - val_accuracy: 0.9806\n"]}]},{"cell_type":"code","source":["predict=model.predict(x_test_final)\n","predict"],"metadata":{"id":"CFy30WfJWZgB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675687900602,"user_tz":-120,"elapsed":480,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}},"outputId":"1511d1bb-cf93-48fd-c400-1b83c66ace2c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["313/313 [==============================] - 1s 2ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["array([[1.0032170e-06, 3.7401915e-07, 3.7730046e-05, ..., 9.9945754e-01,\n","        1.6641516e-06, 9.6814065e-06],\n","       [3.8053761e-07, 1.2624753e-04, 9.9976951e-01, ..., 4.7345457e-12,\n","        4.9280393e-06, 1.7760912e-12],\n","       [3.7444174e-06, 9.9793601e-01, 3.4068999e-04, ..., 9.6008327e-04,\n","        5.9778930e-04, 5.1569673e-06],\n","       ...,\n","       [4.3569295e-10, 5.4191746e-10, 1.6087359e-10, ..., 5.3144799e-06,\n","        1.8387524e-06, 1.8017310e-05],\n","       [2.8192098e-08, 3.6832304e-09, 2.8960545e-10, ..., 7.0342887e-10,\n","        1.1169020e-05, 3.8783288e-10],\n","       [6.7014703e-08, 1.3894239e-10, 1.7721001e-07, ..., 9.9862588e-13,\n","        2.3605220e-09, 2.8051506e-10]], dtype=float32)"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["plt.plot(results.history['accuracy'])\n","plt.plot(results.history['val_accuracy'])\n","plt.legend(['Training', 'Validation'])\n","plt.title('Training and Validation losses')\n","plt.xlabel('epoches')\n","plt.ylabel('accuracy')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":312},"id":"lmjrVNQjfLqN","executionInfo":{"status":"ok","timestamp":1675687901534,"user_tz":-120,"elapsed":947,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}},"outputId":"09f00a48-062e-4115-a06c-00c9fcee8a50"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'accuracy')"]},"metadata":{},"execution_count":9},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[],"metadata":{"id":"3NcQcu-Oyr-Z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.plot(results.history['loss'])\n","plt.plot(results.history['val_loss'])\n","plt.legend(['Training', 'Validation'])\n","plt.title('Training and Validation losses')\n","plt.xlabel('epoches')\n","plt.ylabel('loss')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":312},"executionInfo":{"status":"ok","timestamp":1675688103506,"user_tz":-120,"elapsed":438,"user":{"displayName":"nada mohamed","userId":"02411531856657226807"}},"outputId":"8be18377-9dc1-40df-f0e8-c2ab2fffd04e","id":"6wk1fqDTys0e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'loss')"]},"metadata":{},"execution_count":10},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}