{ "cells": [ { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "from keras.models import Sequential\n", "from keras.layers import Dense, Activation\n", "from keras.datasets import boston_housing" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "(X_train, Y_train), (X_test, Y_test) = boston_housing.load_data()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1000\n", "404/404 [==============================] - 0s - loss: 152.4799 - mean_squared_error: 152.4799 - mean_absolute_error: 9.6304 \n", "Epoch 2/1000\n", "404/404 [==============================] - 0s - loss: 75.1149 - mean_squared_error: 75.1149 - mean_absolute_error: 6.0855 \n", "Epoch 3/1000\n", "404/404 [==============================] - 0s - loss: 69.5583 - mean_squared_error: 69.5583 - mean_absolute_error: 6.0167 \n", "Epoch 4/1000\n", "404/404 [==============================] - 0s - loss: 68.0251 - mean_squared_error: 68.0251 - mean_absolute_error: 5.8919 \n", "Epoch 5/1000\n", "404/404 [==============================] - 0s - loss: 66.0708 - mean_squared_error: 66.0708 - mean_absolute_error: 5.8364 \n", "Epoch 6/1000\n", "404/404 [==============================] - 0s - loss: 62.0408 - mean_squared_error: 62.0408 - mean_absolute_error: 5.7741 \n", "Epoch 7/1000\n", "404/404 [==============================] - 0s - loss: 61.8625 - mean_squared_error: 61.8625 - mean_absolute_error: 5.7584 \n", "Epoch 8/1000\n", "404/404 [==============================] - 0s - loss: 61.3506 - mean_squared_error: 61.3506 - mean_absolute_error: 5.6645 \n", "Epoch 9/1000\n", "404/404 [==============================] - 0s - loss: 60.2124 - mean_squared_error: 60.2124 - mean_absolute_error: 5.6132 \n", "Epoch 10/1000\n", "404/404 [==============================] - 0s - loss: 58.6913 - mean_squared_error: 58.6913 - mean_absolute_error: 5.6017 \n", "Epoch 11/1000\n", "404/404 [==============================] - 0s - loss: 57.9432 - mean_squared_error: 57.9432 - mean_absolute_error: 5.5494 \n", "Epoch 12/1000\n", "404/404 [==============================] - 0s - loss: 56.8824 - mean_squared_error: 56.8824 - mean_absolute_error: 5.4260 \n", "Epoch 13/1000\n", "404/404 [==============================] - 0s - loss: 56.3089 - mean_squared_error: 56.3089 - mean_absolute_error: 5.4978 \n", "Epoch 14/1000\n", "404/404 [==============================] - 0s - loss: 54.2514 - mean_squared_error: 54.2514 - mean_absolute_error: 5.3452 \n", "Epoch 15/1000\n", "404/404 [==============================] - 0s - loss: 54.5575 - mean_squared_error: 54.5575 - mean_absolute_error: 5.3789 \n", "Epoch 16/1000\n", "404/404 [==============================] - 0s - loss: 53.3577 - mean_squared_error: 53.3577 - mean_absolute_error: 5.4448 \n", "Epoch 17/1000\n", "404/404 [==============================] - 0s - loss: 53.6863 - mean_squared_error: 53.6863 - mean_absolute_error: 5.3341 \n", "Epoch 18/1000\n", "404/404 [==============================] - 0s - loss: 51.5926 - mean_squared_error: 51.5926 - mean_absolute_error: 5.3244 \n", "Epoch 19/1000\n", "404/404 [==============================] - 0s - loss: 51.4714 - mean_squared_error: 51.4714 - mean_absolute_error: 5.3381 \n", "Epoch 20/1000\n", "404/404 [==============================] - 0s - loss: 51.3925 - mean_squared_error: 51.3925 - mean_absolute_error: 5.2532 \n", "Epoch 21/1000\n", "404/404 [==============================] - 0s - loss: 51.0795 - mean_squared_error: 51.0795 - mean_absolute_error: 5.2683 \n", "Epoch 22/1000\n", "404/404 [==============================] - 0s - loss: 50.5661 - mean_squared_error: 50.5661 - mean_absolute_error: 5.3529 \n", "Epoch 23/1000\n", "404/404 [==============================] - 0s - loss: 49.9413 - mean_squared_error: 49.9413 - mean_absolute_error: 5.2917 \n", "Epoch 24/1000\n", "404/404 [==============================] - 0s - loss: 49.3967 - mean_squared_error: 49.3967 - mean_absolute_error: 5.2620 \n", "Epoch 25/1000\n", "404/404 [==============================] - 0s - loss: 49.0987 - mean_squared_error: 49.0987 - mean_absolute_error: 5.3054 \n", "Epoch 26/1000\n", "404/404 [==============================] - 0s - loss: 48.0337 - mean_squared_error: 48.0337 - mean_absolute_error: 5.1885 \n", "Epoch 27/1000\n", "404/404 [==============================] - 0s - loss: 48.1433 - mean_squared_error: 48.1433 - mean_absolute_error: 5.2667 \n", "Epoch 28/1000\n", "404/404 [==============================] - 0s - loss: 47.8221 - mean_squared_error: 47.8221 - mean_absolute_error: 5.1985 \n", "Epoch 29/1000\n", "404/404 [==============================] - 0s - loss: 47.7517 - mean_squared_error: 47.7517 - mean_absolute_error: 5.1980 \n", "Epoch 30/1000\n", "404/404 [==============================] - 0s - loss: 47.9040 - mean_squared_error: 47.9040 - mean_absolute_error: 5.1902 \n", "Epoch 31/1000\n", "404/404 [==============================] - 0s - loss: 47.2879 - mean_squared_error: 47.2879 - mean_absolute_error: 5.1726 \n", "Epoch 32/1000\n", "404/404 [==============================] - 0s - loss: 47.2588 - mean_squared_error: 47.2588 - mean_absolute_error: 5.1973 \n", "Epoch 33/1000\n", "404/404 [==============================] - 0s - loss: 46.4283 - mean_squared_error: 46.4283 - mean_absolute_error: 5.1925 \n", "Epoch 34/1000\n", "404/404 [==============================] - 0s - loss: 44.8901 - mean_squared_error: 44.8901 - mean_absolute_error: 5.0216 \n", "Epoch 35/1000\n", "404/404 [==============================] - 0s - loss: 45.7442 - mean_squared_error: 45.7442 - mean_absolute_error: 5.0706 \n", "Epoch 36/1000\n", "404/404 [==============================] - 0s - loss: 45.3987 - mean_squared_error: 45.3987 - mean_absolute_error: 5.1026 \n", "Epoch 37/1000\n", "404/404 [==============================] - 0s - loss: 44.7941 - mean_squared_error: 44.7941 - mean_absolute_error: 5.0609 \n", "Epoch 38/1000\n", "404/404 [==============================] - 0s - loss: 45.3308 - mean_squared_error: 45.3308 - mean_absolute_error: 5.1261 \n", "Epoch 39/1000\n", "404/404 [==============================] - 0s - loss: 44.6182 - mean_squared_error: 44.6182 - mean_absolute_error: 5.0569 \n", "Epoch 40/1000\n", "404/404 [==============================] - 0s - loss: 44.9005 - mean_squared_error: 44.9005 - mean_absolute_error: 5.0373 \n", "Epoch 41/1000\n", "404/404 [==============================] - 0s - loss: 44.5237 - mean_squared_error: 44.5237 - mean_absolute_error: 5.0678 \n", "Epoch 42/1000\n", "404/404 [==============================] - 0s - loss: 43.4067 - mean_squared_error: 43.4067 - mean_absolute_error: 4.9937 \n", "Epoch 43/1000\n", "404/404 [==============================] - 0s - loss: 44.1174 - mean_squared_error: 44.1174 - mean_absolute_error: 5.0577 \n", "Epoch 44/1000\n", "404/404 [==============================] - 0s - loss: 43.6039 - mean_squared_error: 43.6039 - mean_absolute_error: 5.0276 \n", "Epoch 45/1000\n", "404/404 [==============================] - 0s - loss: 43.3509 - mean_squared_error: 43.3509 - mean_absolute_error: 5.0220 \n", "Epoch 46/1000\n", "404/404 [==============================] - 0s - loss: 44.1222 - mean_squared_error: 44.1222 - mean_absolute_error: 5.0517 \n", "Epoch 47/1000\n", "404/404 [==============================] - 0s - loss: 43.8576 - mean_squared_error: 43.8576 - mean_absolute_error: 5.0526 \n", "Epoch 48/1000\n", "404/404 [==============================] - 0s - loss: 43.5052 - mean_squared_error: 43.5052 - mean_absolute_error: 4.9949 \n", "Epoch 49/1000\n", "404/404 [==============================] - 0s - loss: 43.7530 - mean_squared_error: 43.7530 - mean_absolute_error: 5.0504 \n", "Epoch 50/1000\n", "404/404 [==============================] - 0s - loss: 43.1814 - mean_squared_error: 43.1814 - mean_absolute_error: 4.9949 \n", "Epoch 51/1000\n", "404/404 [==============================] - 0s - loss: 43.6105 - mean_squared_error: 43.6105 - mean_absolute_error: 5.0426 \n", "Epoch 52/1000\n", "404/404 [==============================] - 0s - loss: 42.3304 - mean_squared_error: 42.3304 - mean_absolute_error: 4.9442 \n", "Epoch 53/1000\n", "404/404 [==============================] - 0s - loss: 43.0791 - mean_squared_error: 43.0791 - mean_absolute_error: 4.9500 \n", "Epoch 54/1000\n", "404/404 [==============================] - 0s - loss: 42.8263 - mean_squared_error: 42.8263 - mean_absolute_error: 5.0083 \n", "Epoch 55/1000\n", "404/404 [==============================] - 0s - loss: 41.9571 - mean_squared_error: 41.9571 - mean_absolute_error: 4.9297 \n", "Epoch 56/1000\n", "404/404 [==============================] - 0s - loss: 42.5178 - mean_squared_error: 42.5178 - mean_absolute_error: 4.9655 \n", "Epoch 57/1000\n", "404/404 [==============================] - 0s - loss: 41.8807 - mean_squared_error: 41.8807 - mean_absolute_error: 4.8936 \n", "Epoch 58/1000\n", "404/404 [==============================] - 0s - loss: 41.4168 - mean_squared_error: 41.4168 - mean_absolute_error: 4.8426 \n", "Epoch 59/1000\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "404/404 [==============================] - 0s - loss: 41.7527 - mean_squared_error: 41.7527 - mean_absolute_error: 4.8914 \n", "Epoch 60/1000\n", "404/404 [==============================] - 0s - loss: 41.6322 - mean_squared_error: 41.6322 - mean_absolute_error: 4.8575 \n", "Epoch 61/1000\n", "404/404 [==============================] - 0s - loss: 41.4964 - mean_squared_error: 41.4964 - mean_absolute_error: 4.9406 \n", "Epoch 62/1000\n", "404/404 [==============================] - 0s - loss: 41.6924 - mean_squared_error: 41.6924 - mean_absolute_error: 4.9263 \n", "Epoch 63/1000\n", "404/404 [==============================] - 0s - loss: 41.0838 - mean_squared_error: 41.0838 - mean_absolute_error: 4.9166 \n", "Epoch 64/1000\n", "404/404 [==============================] - 0s - loss: 41.6591 - mean_squared_error: 41.6591 - mean_absolute_error: 4.9054 \n", "Epoch 65/1000\n", "404/404 [==============================] - 0s - loss: 40.7573 - mean_squared_error: 40.7573 - mean_absolute_error: 4.8406 \n", "Epoch 66/1000\n", "404/404 [==============================] - 0s - loss: 41.3372 - mean_squared_error: 41.3372 - mean_absolute_error: 4.8742 \n", "Epoch 67/1000\n", "404/404 [==============================] - 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0s - loss: 40.4389 - mean_squared_error: 40.4389 - mean_absolute_error: 4.8168 \n", "Epoch 75/1000\n", "404/404 [==============================] - 0s - loss: 39.4356 - mean_squared_error: 39.4356 - mean_absolute_error: 4.7386 \n", "Epoch 76/1000\n", "404/404 [==============================] - 0s - loss: 40.1033 - mean_squared_error: 40.1033 - mean_absolute_error: 4.7885 \n", "Epoch 77/1000\n", "404/404 [==============================] - 0s - loss: 40.1261 - mean_squared_error: 40.1261 - mean_absolute_error: 4.8373 \n", "Epoch 78/1000\n", "404/404 [==============================] - 0s - loss: 38.6769 - mean_squared_error: 38.6769 - mean_absolute_error: 4.6951 \n", "Epoch 79/1000\n", "404/404 [==============================] - 0s - loss: 39.4568 - mean_squared_error: 39.4568 - mean_absolute_error: 4.7575 \n", "Epoch 80/1000\n", "404/404 [==============================] - 0s - loss: 39.9585 - mean_squared_error: 39.9585 - mean_absolute_error: 4.7386 \n", "Epoch 81/1000\n", "404/404 [==============================] - 0s - loss: 40.0942 - mean_squared_error: 40.0942 - mean_absolute_error: 4.8245 \n", "Epoch 82/1000\n", "404/404 [==============================] - 0s - loss: 39.4487 - mean_squared_error: 39.4487 - mean_absolute_error: 4.7240 \n", "Epoch 83/1000\n", "404/404 [==============================] - 0s - loss: 39.6566 - mean_squared_error: 39.6566 - mean_absolute_error: 4.7602 \n", "Epoch 84/1000\n", "404/404 [==============================] - 0s - loss: 39.7058 - mean_squared_error: 39.7058 - mean_absolute_error: 4.8047 \n", "Epoch 85/1000\n", "404/404 [==============================] - 0s - loss: 38.9166 - mean_squared_error: 38.9166 - mean_absolute_error: 4.6670 \n", "Epoch 86/1000\n", "404/404 [==============================] - 0s - loss: 39.0315 - mean_squared_error: 39.0315 - mean_absolute_error: 4.6965 \n", "Epoch 87/1000\n", "404/404 [==============================] - 0s - loss: 38.8982 - mean_squared_error: 38.8982 - mean_absolute_error: 4.7190 \n", "Epoch 88/1000\n", "404/404 [==============================] - 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0s - loss: 37.7679 - mean_squared_error: 37.7679 - mean_absolute_error: 4.6437 \n", "Epoch 103/1000\n", "404/404 [==============================] - 0s - loss: 37.9556 - mean_squared_error: 37.9556 - mean_absolute_error: 4.5717 \n", "Epoch 104/1000\n", "404/404 [==============================] - 0s - loss: 37.7755 - mean_squared_error: 37.7755 - mean_absolute_error: 4.5860 \n", "Epoch 105/1000\n", "404/404 [==============================] - 0s - loss: 37.8125 - mean_squared_error: 37.8125 - mean_absolute_error: 4.5520 \n", "Epoch 106/1000\n", "404/404 [==============================] - 0s - loss: 37.9617 - mean_squared_error: 37.9617 - mean_absolute_error: 4.6369 \n", "Epoch 107/1000\n", "404/404 [==============================] - 0s - loss: 37.8995 - mean_squared_error: 37.8995 - mean_absolute_error: 4.5894 \n", "Epoch 108/1000\n", "404/404 [==============================] - 0s - loss: 37.3507 - mean_squared_error: 37.3507 - mean_absolute_error: 4.5714 \n", "Epoch 109/1000\n", "404/404 [==============================] - 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0s - loss: 26.2807 - mean_squared_error: 26.2807 - mean_absolute_error: 3.4640 \n", "Epoch 976/1000\n", "404/404 [==============================] - 0s - loss: 25.8742 - mean_squared_error: 25.8742 - mean_absolute_error: 3.4560 \n", "Epoch 977/1000\n", "404/404 [==============================] - 0s - loss: 25.4864 - mean_squared_error: 25.4864 - mean_absolute_error: 3.5190 \n", "Epoch 978/1000\n", "404/404 [==============================] - 0s - loss: 25.0220 - mean_squared_error: 25.0220 - mean_absolute_error: 3.4531 \n", "Epoch 979/1000\n", "404/404 [==============================] - 0s - loss: 25.8328 - mean_squared_error: 25.8328 - mean_absolute_error: 3.5421 \n", "Epoch 980/1000\n", "404/404 [==============================] - 0s - loss: 24.9428 - mean_squared_error: 24.9428 - mean_absolute_error: 3.5588 \n", "Epoch 981/1000\n", "404/404 [==============================] - 0s - loss: 26.3633 - mean_squared_error: 26.3633 - mean_absolute_error: 3.4875 \n", "Epoch 982/1000\n", "404/404 [==============================] - 0s - loss: 25.8253 - mean_squared_error: 25.8253 - mean_absolute_error: 3.5116 \n", "Epoch 983/1000\n", "404/404 [==============================] - 0s - loss: 26.4831 - mean_squared_error: 26.4831 - mean_absolute_error: 3.5154 \n", "Epoch 984/1000\n", "404/404 [==============================] - 0s - loss: 25.5362 - mean_squared_error: 25.5362 - mean_absolute_error: 3.5259 \n", "Epoch 985/1000\n", "404/404 [==============================] - 0s - loss: 25.7780 - mean_squared_error: 25.7780 - mean_absolute_error: 3.5197 \n", "Epoch 986/1000\n", "404/404 [==============================] - 0s - loss: 26.3967 - mean_squared_error: 26.3967 - mean_absolute_error: 3.5408 \n", "Epoch 987/1000\n", "404/404 [==============================] - 0s - loss: 25.6272 - mean_squared_error: 25.6272 - mean_absolute_error: 3.5313 \n", "Epoch 988/1000\n", "404/404 [==============================] - 0s - loss: 25.1378 - mean_squared_error: 25.1378 - mean_absolute_error: 3.5096 \n", "Epoch 989/1000\n", "404/404 [==============================] - 0s - loss: 26.5154 - mean_squared_error: 26.5154 - mean_absolute_error: 3.4955 \n", "Epoch 990/1000\n", "404/404 [==============================] - 0s - loss: 25.4443 - mean_squared_error: 25.4443 - mean_absolute_error: 3.4675 \n", "Epoch 991/1000\n", "404/404 [==============================] - 0s - loss: 26.1981 - mean_squared_error: 26.1981 - mean_absolute_error: 3.5019 \n", "Epoch 992/1000\n", "404/404 [==============================] - 0s - loss: 25.7281 - mean_squared_error: 25.7281 - mean_absolute_error: 3.4968 \n", "Epoch 993/1000\n", "404/404 [==============================] - 0s - loss: 25.1960 - mean_squared_error: 25.1960 - mean_absolute_error: 3.4701 \n", "Epoch 994/1000\n", "404/404 [==============================] - 0s - loss: 25.7093 - mean_squared_error: 25.7093 - mean_absolute_error: 3.4890 \n", "Epoch 995/1000\n", "404/404 [==============================] - 0s - loss: 26.3761 - mean_squared_error: 26.3761 - mean_absolute_error: 3.4950 \n", "Epoch 996/1000\n", "404/404 [==============================] - 0s - loss: 25.0795 - mean_squared_error: 25.0795 - mean_absolute_error: 3.4556 \n", "Epoch 997/1000\n", "404/404 [==============================] - 0s - loss: 25.8258 - mean_squared_error: 25.8258 - mean_absolute_error: 3.5714 \n", "Epoch 998/1000\n", "404/404 [==============================] - 0s - loss: 25.9369 - mean_squared_error: 25.9369 - mean_absolute_error: 3.5279 \n", "Epoch 999/1000\n", "404/404 [==============================] - 0s - loss: 25.7215 - mean_squared_error: 25.7215 - mean_absolute_error: 3.5185 \n", "Epoch 1000/1000\n", "404/404 [==============================] - 0s - loss: 25.6472 - mean_squared_error: 25.6472 - mean_absolute_error: 3.5188 \n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nFeatures = X_train.shape[1]\n", "\n", "model = Sequential()\n", "model.add(Dense(1, input_shape=(nFeatures,), kernel_initializer='uniform'))\n", "model.add(Activation('linear'))\n", "\n", "model.compile(optimizer='rmsprop', loss='mse', metrics=['mse', 'mae'])\n", "\n", "model.fit(X_train, Y_train, batch_size=4, epochs=1000)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_21 (Dense) (None, 1) 14 \n", "_________________________________________________________________\n", "activation_2 (Activation) (None, 1) 0 \n", "=================================================================\n", "Total params: 14\n", "Trainable params: 14\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "\r", " 32/102 [========>.....................] - ETA: 0s" ] }, { "data": { "text/plain": [ "[23.216887230966606, 23.216887230966606, 3.4574407783209109]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.summary()\n", "\n", "model.evaluate(X_test, Y_test, verbose=True)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "Y_pred = model.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 7.2 18.8 19. 27. 22.2]\n", "[ 7.19826889 18.25551987 21.38941193 29.28108978 23.72646713]\n" ] } ], "source": [ "print Y_test[:5]\n", "print Y_pred[:5,0]" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }