training with result, sorta+

jupyter/wut-ml.ipynb

@@ -626,27 +626,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 319,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TRAINING info\n"
+     ]
+    }
+   ],
    "source": [
-    "print(\"TRAINING\")"
+    "print(\"TRAINING info\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 320,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data/train\n"
+     ]
+    }
+   ],
    "source": [
     "print(train_dir)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 321,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data/train/good\n",
+      "data/train/bad\n"
+     ]
+    }
+   ],
    "source": [
     "print(train_good_dir)\n",
     "print(train_bad_dir)"
@@ -654,43 +679,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 322,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<tensorflow.python.keras.preprocessing.image.ImageDataGenerator object at 0x7f0864059e80>\n"
+     ]
+    }
+   ],
    "source": [
     "print(train_image_generator)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 323,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<keras_preprocessing.image.directory_iterator.DirectoryIterator object at 0x7f084076d048>\n"
+     ]
+    }
+   ],
    "source": [
     "print(train_data_gen)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 332,
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(sample_train_images)"
+    "#print(sample_train_images)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 325,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<tensorflow.python.keras.callbacks.History object at 0x7f08642b4390>\n"
+     ]
+    }
+   ],
    "source": [
     "print(history)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 334,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -721,9 +770,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 327,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predict\n"
+     ]
+    }
+   ],
    "source": [
     "# https://keras.io/models/sequential/\n",
     "print(\"predict\")"
@@ -731,9 +788,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 328,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4/4 [==============================] - 1s 220ms/step\n"
+     ]
+    }
+   ],
    "source": [
     "pred=model.predict_generator(test_data_gen,\n",
     "steps=4,\n",
@@ -742,9 +807,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 335,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 - 0s\n",
+      "end predict\n"
+     ]
+    }
+   ],
    "source": [
     "prediction = model.predict(\n",
     "    x=test_data_gen,\n",
@@ -762,7 +836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 336,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -771,9 +845,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 337,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.9941876]]\n"
+     ]
+    }
+   ],
    "source": [
     "# Show prediction score\n",
     "print(prediction)"
@@ -781,23 +863,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 338,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[False]]\n"
+     ]
+    }
+   ],
    "source": [
     "#prediction_bool = (prediction >0.5)\n",
     "prediction_bool = (prediction == 1)\n",
     "print(prediction_bool)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 339,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = prediction_bool.astype(int)\n",
+    "print(predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 345,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Observation: bad\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make final prediction\n",
+    "if prediction_bool == False:\n",
+    "  rating = 'bad'\n",
+    "else:\n",
+    "  rating = 'good'\n",
+    "print('Observation: %s' % (rating))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "predictions = prediction_bool.astype(int)\n",
+    "barf()"
-    "print(predictions)"
    ]
   },
   {
@@ -899,69 +1056,6 @@
    "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1091,15 +1185,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
+   "source": []
-    "# Make final prediction\n",
-    "#if prediction[0][0] == 0:\n",
-    "if prediction == [[0]]:\n",
-    "  rating = 'bad'\n",
-    "else:\n",
-    "  rating = 'good'\n",
-    "print('Observation: %s' % (rating))"
-   ]
   },
   {
    "cell_type": "code",