mirror of
https://github.com/wassname/cryptokitties_genetics.git
synced 2026-06-27 16:30:06 +08:00
fix dummy loss
This commit is contained in:
wassname
+93
-48
@@ -4329,11 +4329,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 845,
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"execution_count": 853,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-12-09T05:13:10.132288Z",
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"start_time": "2017-12-09T05:13:09.985293Z"
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"end_time": "2017-12-09T05:17:03.149046Z",
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"start_time": "2017-12-09T05:17:03.020120Z"
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}
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},
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"outputs": [
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@@ -4341,8 +4341,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"mean mean absolute error ($) 18.9902441713\n",
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"median mean absolute error ($) 18.9369209377\n"
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"mean mean absolute error ($) 28.2072223243\n",
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"median mean absolute error ($) 28.1958325447\n"
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]
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}
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],
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@@ -4353,10 +4353,12 @@
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" clf = DummyRegressor(strategy=strategy)\n",
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" clf.fit(X_train, y_train)\n",
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" y_pred = clf.predict(X_test)\n",
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" mae = sklearn.metrics.mean_absolute_error(y_test, y_pred)\n",
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" \n",
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" if use_log_y:\n",
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" mae = np.exp(mae)\n",
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" print(strategy,'mean absolute error ($)', np.exp(mae)) "
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" mae = sklearn.metrics.mean_absolute_error(np.exp(y_test), np.exp(y_pred))\n",
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" else:\n",
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" mae = sklearn.metrics.mean_absolute_error(y_test, y_pred)\n",
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" print(strategy,'mean absolute error ($)', mae)"
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]
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},
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{
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@@ -4374,11 +4376,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 846,
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"execution_count": 857,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2017-12-09T05:13:10.338171Z",
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"start_time": "2017-12-09T05:13:10.262168Z"
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"end_time": "2017-12-09T05:17:58.854965Z",
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"start_time": "2017-12-09T05:17:58.779727Z"
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}
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},
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"outputs": [
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@@ -4389,17 +4391,17 @@
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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"input_73 (InputLayer) (None, 517) 0 \n",
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"input_74 (InputLayer) (None, 517) 0 \n",
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"_________________________________________________________________\n",
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"dense_121 (Dense) (None, 128) 66304 \n",
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"dense_126 (Dense) (None, 128) 66304 \n",
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"_________________________________________________________________\n",
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"dense_122 (Dense) (None, 64) 8256 \n",
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"dense_127 (Dense) (None, 64) 8256 \n",
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"_________________________________________________________________\n",
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"dense_123 (Dense) (None, 32) 2080 \n",
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"dense_128 (Dense) (None, 32) 2080 \n",
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"_________________________________________________________________\n",
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"dense_124 (Dense) (None, 16) 528 \n",
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"dense_129 (Dense) (None, 16) 528 \n",
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"_________________________________________________________________\n",
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"dense_125 (Dense) (None, 1) 17 \n",
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"dense_130 (Dense) (None, 1) 17 \n",
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"=================================================================\n",
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"Total params: 77,185\n",
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"Trainable params: 77,185\n",
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@@ -4426,10 +4428,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 858,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2017-12-09T05:13:32.909Z"
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"end_time": "2017-12-09T05:18:17.021414Z",
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"start_time": "2017-12-09T05:17:59.093828Z"
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},
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"scrolled": true
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},
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@@ -4440,31 +4443,39 @@
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"text": [
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"Train on 19963 samples, validate on 4991 samples\n",
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"Epoch 1/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.9538 - acc: 0.0000e+00 - val_loss: 0.9735 - val_acc: 0.0000e+00\n",
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"19963/19963 [==============================] - 3s - loss: 1.0874 - acc: 0.0000e+00 - val_loss: 1.0069 - val_acc: 0.0000e+00\n",
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"Epoch 2/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.9482 - acc: 0.0000e+00 - val_loss: 0.9737 - val_acc: 0.0000e+00\n",
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"19963/19963 [==============================] - 2s - loss: 1.0223 - acc: 0.0000e+00 - val_loss: 0.9994 - val_acc: 0.0000e+00\n",
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"Epoch 3/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.9393 - acc: 0.0000e+00 - val_loss: 0.9669 - val_acc: 0.0000e+00\n",
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"19963/19963 [==============================] - 2s - loss: 1.0012 - acc: 0.0000e+00 - val_loss: 0.9899 - val_acc: 0.0000e+00\n",
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"Epoch 4/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.9357 - acc: 0.0000e+00 - val_loss: 0.9589 - val_acc: 0.0000e+00\n",
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"19963/19963 [==============================] - 2s - loss: 0.9885 - acc: 0.0000e+00 - val_loss: 0.9825 - val_acc: 0.0000e+00\n",
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"Epoch 5/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.9279 - acc: 0.0000e+00 - val_loss: 0.9663 - val_acc: 0.0000e+00\n",
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"19963/19963 [==============================] - 2s - loss: 0.9736 - acc: 0.0000e+00 - val_loss: 0.9748 - val_acc: 0.0000e+00\n",
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"Epoch 6/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.9232 - acc: 0.0000e+00 - val_loss: 0.9615 - val_acc: 0.0000e+00\n",
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"19963/19963 [==============================] - 2s - loss: 0.9646 - acc: 0.0000e+00 - val_loss: 0.9937 - val_acc: 0.0000e+00\n",
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"Epoch 7/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.9145 - acc: 0.0000e+00 - val_loss: 0.9618 - val_acc: 0.0000e+00\n",
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"Epoch 8/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.9102 - acc: 0.0000e+00 - val_loss: 0.9617 - val_acc: 0.0000e+00\n",
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"Epoch 9/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.9022 - acc: 0.0000e+00 - val_loss: 0.9765 - val_acc: 0.0000e+00\n",
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"Epoch 10/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.8954 - acc: 0.0000e+00 - val_loss: 0.9623 - val_acc: 0.0000e+00\n",
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"Epoch 11/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.8892 - acc: 0.0000e+00 - val_loss: 0.9680 - val_acc: 0.0000e+00\n",
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"Epoch 12/100\n",
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"19963/19963 [==============================] - 2s - loss: 0.8811 - acc: 0.0000e+00 - val_loss: 0.9775 - val_acc: 0.0000e+00\n",
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"Epoch 13/100\n",
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" 1280/19963 [>.............................] - ETA: 2s - loss: 0.9036 - acc: 0.0000e+00"
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" 4352/19963 [=====>........................] - ETA: 2s - loss: 0.9492 - acc: 0.0000e+00"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-858-f05a8e800ab9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m 868\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 869\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 870\u001b[0;31m initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m 871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 872\u001b[0m def evaluate(self, x, y, batch_size=32, verbose=1,\n",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m 1505\u001b[0m \u001b[0mval_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1506\u001b[0m \u001b[0mcallback_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1507\u001b[0;31m initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m 1508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m 1154\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1155\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1156\u001b[0;31m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1157\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1158\u001b[0m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2267\u001b[0m updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m 2268\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2269\u001b[0;31m **self.session_kwargs)\n\u001b[0m\u001b[1;32m 2270\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 896\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1122\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1123\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1124\u001b[0;31m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m 1125\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1126\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1320\u001b[0m return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1321\u001b[0;31m options, run_metadata)\n\u001b[0m\u001b[1;32m 1322\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1323\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1325\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1326\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1327\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1328\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1329\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/.pyenv/versions/3.5.3/envs/jupyter3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1304\u001b[0m return tf_session.TF_Run(session, options,\n\u001b[1;32m 1305\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1308\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -4477,6 +4488,7 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2017-12-09T05:15:37.170853Z",
|
||||
"start_time": "2017-12-09T05:13:35.064Z"
|
||||
}
|
||||
},
|
||||
@@ -4496,32 +4508,65 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 859,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2017-12-09T05:13:35.948Z"
|
||||
"end_time": "2017-12-09T05:18:18.938022Z",
|
||||
"start_time": "2017-12-09T05:18:18.740891Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2304/2773 [=======================>......] - ETA: 0s"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'acc': 0.0, 'loss': 0.95822394375675557}"
|
||||
]
|
||||
},
|
||||
"execution_count": 859,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"metrics = model.evaluate(X_test,y_test)\n",
|
||||
"metrics = dict(zip(model.metrics_names, metrics))\n",
|
||||
"if use_log_mae:\n",
|
||||
" metrics['mae_dollars']=np.exp(metrics['loss'])\n",
|
||||
"metrics"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 860,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2017-12-09T05:01:44.599815Z",
|
||||
"start_time": "2017-12-09T05:01:44.106757Z"
|
||||
"end_time": "2017-12-09T05:18:19.875146Z",
|
||||
"start_time": "2017-12-09T05:18:19.404365Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"26.219251685788045"
|
||||
]
|
||||
},
|
||||
"execution_count": 860,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# I got ~26 which is not great\n",
|
||||
"y_pred = model.predict(X_test)\n",
|
||||
"mae = sklearn.metrics.mean_absolute_error(np.exp(y_test), np.exp(y_pred))\n",
|
||||
"mae"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
|
||||
+2212
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user