{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:180: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n", "WARNING:tensorflow:From D:\\ProgramData\\Anaconda3\\envs\\deep\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n", "\n", "Epoch 1/150\n", "768/768 [==============================] - 2s 3ms/step - loss: 3.1948 - accuracy: 0.5833\n", "Epoch 2/150\n", "768/768 [==============================] - 0s 103us/step - loss: 0.9527 - accuracy: 0.5781\n", "Epoch 3/150\n", "768/768 [==============================] - 0s 106us/step - loss: 0.7632 - accuracy: 0.6302\n", "Epoch 4/150\n", "768/768 [==============================] - 0s 101us/step - loss: 0.7209 - accuracy: 0.6510\n", "Epoch 5/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.6933 - accuracy: 0.6693\n", "Epoch 6/150\n", "768/768 [==============================] - 0s 105us/step - loss: 0.6687 - accuracy: 0.6875\n", "Epoch 7/150\n", "768/768 [==============================] - 0s 105us/step - loss: 0.6621 - accuracy: 0.6758\n", "Epoch 8/150\n", "768/768 [==============================] - 0s 108us/step - loss: 0.6493 - accuracy: 0.6862\n", "Epoch 9/150\n", "768/768 [==============================] - 0s 106us/step - loss: 0.6340 - accuracy: 0.6953\n", "Epoch 10/150\n", "768/768 [==============================] - 0s 105us/step - loss: 0.6417 - accuracy: 0.6797\n", "Epoch 11/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.6576 - accuracy: 0.6667\n", "Epoch 12/150\n", "768/768 [==============================] - 0s 108us/step - loss: 0.6506 - accuracy: 0.6810\n", "Epoch 13/150\n", "768/768 [==============================] - 0s 106us/step - loss: 0.6332 - accuracy: 0.6758\n", "Epoch 14/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.6211 - accuracy: 0.7044\n", "Epoch 15/150\n", "768/768 [==============================] - 0s 123us/step - loss: 0.6020 - accuracy: 0.7070\n", "Epoch 16/150\n", "768/768 [==============================] - 0s 126us/step - loss: 0.5876 - accuracy: 0.7070\n", "Epoch 17/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.5824 - accuracy: 0.7070\n", "Epoch 18/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.5942 - accuracy: 0.6966\n", "Epoch 19/150\n", "768/768 [==============================] - 0s 112us/step - loss: 0.5741 - accuracy: 0.7044\n", "Epoch 20/150\n", "768/768 [==============================] - 0s 117us/step - loss: 0.5731 - accuracy: 0.7292\n", "Epoch 21/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.5654 - accuracy: 0.7174\n", "Epoch 22/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.5778 - accuracy: 0.7044\n", "Epoch 23/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.5716 - accuracy: 0.7135\n", "Epoch 24/150\n", "768/768 [==============================] - 0s 119us/step - loss: 0.5690 - accuracy: 0.7318\n", "Epoch 25/150\n", "768/768 [==============================] - 0s 119us/step - loss: 0.5554 - accuracy: 0.7448\n", "Epoch 26/150\n", "768/768 [==============================] - 0s 123us/step - loss: 0.5725 - accuracy: 0.7031\n", "Epoch 27/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.5552 - accuracy: 0.7253\n", "Epoch 28/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.5586 - accuracy: 0.7214\n", "Epoch 29/150\n", "768/768 [==============================] - 0s 117us/step - loss: 0.5773 - accuracy: 0.7174\n", "Epoch 30/150\n", "768/768 [==============================] - 0s 117us/step - loss: 0.5614 - accuracy: 0.7188\n", "Epoch 31/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.5713 - accuracy: 0.7109\n", "Epoch 32/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.5654 - accuracy: 0.7161\n", "Epoch 33/150\n", "768/768 [==============================] - 0s 105us/step - loss: 0.5505 - accuracy: 0.7214\n", "Epoch 34/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.5535 - accuracy: 0.7357\n", "Epoch 35/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.5541 - accuracy: 0.7240\n", "Epoch 36/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.5630 - accuracy: 0.7148\n", "Epoch 37/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.5320 - accuracy: 0.7331\n", "Epoch 38/150\n", "768/768 [==============================] - ETA: 0s - loss: 0.4983 - accuracy: 0.74 - 0s 105us/step - loss: 0.5406 - accuracy: 0.7266\n", "Epoch 39/150\n", "768/768 [==============================] - 0s 105us/step - loss: 0.5502 - accuracy: 0.7240\n", "Epoch 40/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.5465 - accuracy: 0.7253\n", "Epoch 41/150\n", "768/768 [==============================] - 0s 108us/step - loss: 0.5448 - accuracy: 0.7227\n", "Epoch 42/150\n", "768/768 [==============================] - 0s 103us/step - loss: 0.5392 - accuracy: 0.7318\n", "Epoch 43/150\n", "768/768 [==============================] - 0s 108us/step - loss: 0.5328 - accuracy: 0.7383\n", "Epoch 44/150\n", "768/768 [==============================] - 0s 105us/step - loss: 0.5360 - accuracy: 0.7474\n", "Epoch 45/150\n", "768/768 [==============================] - 0s 105us/step - loss: 0.5344 - accuracy: 0.7591\n", "Epoch 46/150\n", "768/768 [==============================] - 0s 108us/step - loss: 0.5268 - accuracy: 0.7487\n", "Epoch 47/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.5366 - accuracy: 0.7344\n", "Epoch 48/150\n", "768/768 [==============================] - 0s 107us/step - loss: 0.5349 - accuracy: 0.7409\n", "Epoch 49/150\n", "768/768 [==============================] - 0s 107us/step - loss: 0.5366 - accuracy: 0.7539\n", "Epoch 50/150\n", "768/768 [==============================] - 0s 105us/step - loss: 0.5289 - accuracy: 0.7383\n", "Epoch 51/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.5316 - accuracy: 0.7500\n", "Epoch 52/150\n", "768/768 [==============================] - 0s 103us/step - loss: 0.5419 - accuracy: 0.7305\n", "Epoch 53/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.5372 - accuracy: 0.7461\n", "Epoch 54/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.5435 - accuracy: 0.7331\n", "Epoch 55/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.5207 - accuracy: 0.7422\n", "Epoch 56/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.5302 - accuracy: 0.7422\n", "Epoch 57/150\n", "768/768 [==============================] - 0s 100us/step - loss: 0.5369 - accuracy: 0.7474\n", "Epoch 58/150\n", "768/768 [==============================] - 0s 103us/step - loss: 0.5268 - accuracy: 0.7461\n", "Epoch 59/150\n", "768/768 [==============================] - 0s 101us/step - loss: 0.5132 - accuracy: 0.7552\n", "Epoch 60/150\n", "768/768 [==============================] - 0s 101us/step - loss: 0.5350 - accuracy: 0.7331\n", "Epoch 61/150\n", "768/768 [==============================] - 0s 100us/step - loss: 0.5304 - accuracy: 0.7253\n", "Epoch 62/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.5224 - accuracy: 0.7409\n", "Epoch 63/150\n", "768/768 [==============================] - 0s 108us/step - loss: 0.5472 - accuracy: 0.7253\n", "Epoch 64/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.5369 - accuracy: 0.7331\n", "Epoch 65/150\n", "768/768 [==============================] - 0s 108us/step - loss: 0.5257 - accuracy: 0.7396\n", "Epoch 66/150\n", "768/768 [==============================] - 0s 99us/step - loss: 0.5090 - accuracy: 0.7422\n", "Epoch 67/150\n", "768/768 [==============================] - 0s 99us/step - loss: 0.5180 - accuracy: 0.7435\n", "Epoch 68/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.5192 - accuracy: 0.7500\n", "Epoch 69/150\n", "768/768 [==============================] - 0s 108us/step - loss: 0.5209 - accuracy: 0.7266\n", "Epoch 70/150\n", "768/768 [==============================] - 0s 97us/step - loss: 0.5339 - accuracy: 0.7227\n", "Epoch 71/150\n", "768/768 [==============================] - 0s 101us/step - loss: 0.5228 - accuracy: 0.7409\n", "Epoch 72/150\n", "768/768 [==============================] - 0s 99us/step - loss: 0.5179 - accuracy: 0.7500\n", "Epoch 73/150\n", "768/768 [==============================] - 0s 97us/step - loss: 0.5175 - accuracy: 0.7448\n", "Epoch 74/150\n", "768/768 [==============================] - 0s 96us/step - loss: 0.5156 - accuracy: 0.7461\n", "Epoch 75/150\n", "768/768 [==============================] - 0s 92us/step - loss: 0.5142 - accuracy: 0.7435\n", "Epoch 76/150\n", "768/768 [==============================] - 0s 97us/step - loss: 0.5127 - accuracy: 0.7539\n", "Epoch 77/150\n", "768/768 [==============================] - 0s 106us/step - loss: 0.5236 - accuracy: 0.7487\n", "Epoch 78/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.5173 - accuracy: 0.7500\n", "Epoch 79/150\n", "768/768 [==============================] - 0s 106us/step - loss: 0.5122 - accuracy: 0.7513\n", "Epoch 80/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.5138 - accuracy: 0.7578\n", "Epoch 81/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.5106 - accuracy: 0.7474\n", "Epoch 82/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.4992 - accuracy: 0.7513\n", "Epoch 83/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.5000 - accuracy: 0.7539\n", "Epoch 84/150\n", "768/768 [==============================] - 0s 106us/step - loss: 0.4943 - accuracy: 0.7578\n", "Epoch 85/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.5056 - accuracy: 0.7422\n", "Epoch 86/150\n", "768/768 [==============================] - 0s 108us/step - loss: 0.5067 - accuracy: 0.7435\n", "Epoch 87/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.4967 - accuracy: 0.7526\n", "Epoch 88/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.4976 - accuracy: 0.7604\n", "Epoch 89/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.5038 - accuracy: 0.7721\n", "Epoch 90/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.5066 - accuracy: 0.7500\n", "Epoch 91/150\n", "768/768 [==============================] - 0s 116us/step - loss: 0.4990 - accuracy: 0.7474\n", "Epoch 92/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.5046 - accuracy: 0.7487\n", "Epoch 93/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.4940 - accuracy: 0.7552\n", "Epoch 94/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.4933 - accuracy: 0.7656\n", "Epoch 95/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.5047 - accuracy: 0.7396\n", "Epoch 96/150\n", "768/768 [==============================] - 0s 117us/step - loss: 0.4861 - accuracy: 0.7708\n", "Epoch 97/150\n", "768/768 [==============================] - 0s 105us/step - loss: 0.4905 - accuracy: 0.7773\n", "Epoch 98/150\n", "768/768 [==============================] - 0s 106us/step - loss: 0.4866 - accuracy: 0.7539\n", "Epoch 99/150\n", "768/768 [==============================] - 0s 104us/step - loss: 0.4880 - accuracy: 0.7695\n", "Epoch 100/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.4868 - accuracy: 0.7604\n", "Epoch 101/150\n", "768/768 [==============================] - 0s 119us/step - loss: 0.4856 - accuracy: 0.7786\n", "Epoch 102/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.5008 - accuracy: 0.7513\n", "Epoch 103/150\n", "768/768 [==============================] - 0s 108us/step - loss: 0.4964 - accuracy: 0.7578\n", "Epoch 104/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.4892 - accuracy: 0.7904\n", "Epoch 105/150\n", "768/768 [==============================] - 0s 116us/step - loss: 0.5213 - accuracy: 0.7383\n", "Epoch 106/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.4911 - accuracy: 0.7773\n", "Epoch 107/150\n", "768/768 [==============================] - 0s 112us/step - loss: 0.4899 - accuracy: 0.7721\n", "Epoch 108/150\n", "768/768 [==============================] - 0s 112us/step - loss: 0.5021 - accuracy: 0.7617\n", "Epoch 109/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.4821 - accuracy: 0.7604\n", "Epoch 110/150\n", "768/768 [==============================] - 0s 116us/step - loss: 0.4865 - accuracy: 0.7630\n", "Epoch 111/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.4814 - accuracy: 0.7734\n", "Epoch 112/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.4934 - accuracy: 0.7643\n", "Epoch 113/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.4977 - accuracy: 0.7526\n", "Epoch 114/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.4890 - accuracy: 0.7500\n", "Epoch 115/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.4874 - accuracy: 0.7682\n", "Epoch 116/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.4909 - accuracy: 0.7669\n", "Epoch 117/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.4915 - accuracy: 0.7617\n", "Epoch 118/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.4876 - accuracy: 0.7826\n", "Epoch 119/150\n", "768/768 [==============================] - 0s 112us/step - loss: 0.4865 - accuracy: 0.7669\n", "Epoch 120/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.4972 - accuracy: 0.7630\n", "Epoch 121/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.4951 - accuracy: 0.7617\n", "Epoch 122/150\n", "768/768 [==============================] - 0s 108us/step - loss: 0.4811 - accuracy: 0.7656\n", "Epoch 123/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.4864 - accuracy: 0.7643\n", "Epoch 124/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.4819 - accuracy: 0.7682\n", "Epoch 125/150\n", "768/768 [==============================] - 0s 118us/step - loss: 0.4816 - accuracy: 0.7917\n", "Epoch 126/150\n", "768/768 [==============================] - 0s 112us/step - loss: 0.4781 - accuracy: 0.7643\n", "Epoch 127/150\n", "768/768 [==============================] - 0s 112us/step - loss: 0.4882 - accuracy: 0.7591\n", "Epoch 128/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.4688 - accuracy: 0.7604\n", "Epoch 129/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.4810 - accuracy: 0.7669\n", "Epoch 130/150\n", "768/768 [==============================] - 0s 117us/step - loss: 0.4669 - accuracy: 0.7852\n", "Epoch 131/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.4735 - accuracy: 0.7695\n", "Epoch 132/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.4766 - accuracy: 0.7721\n", "Epoch 133/150\n", "768/768 [==============================] - 0s 112us/step - loss: 0.4793 - accuracy: 0.7630\n", "Epoch 134/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.4809 - accuracy: 0.7643\n", "Epoch 135/150\n", "768/768 [==============================] - 0s 118us/step - loss: 0.4713 - accuracy: 0.7682\n", "Epoch 136/150\n", "768/768 [==============================] - 0s 112us/step - loss: 0.4685 - accuracy: 0.7721\n", "Epoch 137/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.4652 - accuracy: 0.7826\n", "Epoch 138/150\n", "768/768 [==============================] - 0s 119us/step - loss: 0.4755 - accuracy: 0.7773\n", "Epoch 139/150\n", "768/768 [==============================] - 0s 120us/step - loss: 0.4661 - accuracy: 0.7773\n", "Epoch 140/150\n", "768/768 [==============================] - 0s 113us/step - loss: 0.4763 - accuracy: 0.7839\n", "Epoch 141/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.4713 - accuracy: 0.7747\n", "Epoch 142/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.4815 - accuracy: 0.7591\n", "Epoch 143/150\n", "768/768 [==============================] - 0s 105us/step - loss: 0.4739 - accuracy: 0.7708\n", "Epoch 144/150\n", "768/768 [==============================] - 0s 106us/step - loss: 0.4714 - accuracy: 0.7760\n", "Epoch 145/150\n", "768/768 [==============================] - 0s 112us/step - loss: 0.4840 - accuracy: 0.7747\n", "Epoch 146/150\n", "768/768 [==============================] - 0s 116us/step - loss: 0.4945 - accuracy: 0.7565\n", "Epoch 147/150\n", "768/768 [==============================] - 0s 114us/step - loss: 0.4835 - accuracy: 0.7695\n", "Epoch 148/150\n", "768/768 [==============================] - 0s 109us/step - loss: 0.4721 - accuracy: 0.7760\n", "Epoch 149/150\n", "768/768 [==============================] - 0s 116us/step - loss: 0.4692 - accuracy: 0.7760\n", "Epoch 150/150\n", "768/768 [==============================] - 0s 110us/step - loss: 0.4758 - accuracy: 0.7760\n", "768/768 [==============================] - 0s 56us/step\n", "\n", "accuracy: 77.21%\n" ] } ], "source": [ "# Create your first MLP in Keras\n", "from keras.models import Sequential\n", "from keras.layers import Dense\n", "import numpy\n", "# fix random seed for reproducibility\n", "numpy.random.seed(7)\n", "# load pima indians dataset\n", "dataset = numpy.loadtxt(\"pima-indians-diabetes.csv\", delimiter=\",\")\n", "# split into input (X) and output (Y) variables\n", "X = dataset[:, 0:8]\n", "Y = dataset[:, 8]\n", "# create model\n", "model = Sequential()\n", "model.add(Dense(12, input_dim=8, activation='relu'))\n", "model.add(Dense(8, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", "# Compile model\n", "model.compile(loss='binary_crossentropy',\n", " optimizer='adam',\n", " metrics=['accuracy'])\n", "# Fit the model\n", "model.fit(X, Y, epochs=150, batch_size=10)\n", "# evaluate the model\n", "scores = model.evaluate(X, Y)\n", "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1] * 100))" ] } ], "metadata": { "kernelspec": { "display_name": "deep", "language": "python", "name": "deep" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 4 }