trying out keras on the pima-indians-diabetes dataset

tutorial.ipynb

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   "source": [
    "# http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy as np\n",
    "import urllib.request\n",
    "\n",
    "seed = 7\n",
    "np.random.seed(seed)"
   ]
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   "source": [
    "dataset = np.loadtxt(urllib.request.urlopen(\"http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data\"), delimiter=\",\")\n",
    "X = dataset[:, 0:8]\n",
    "Y = dataset[:, 8]\n",
    "len(dataset)"
   ]
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    "dataset[0]"
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     "execution_count": 30,
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   "source": [
    "trainX = X[0:700]\n",
    "trainY = Y[0:700]\n",
    "testX = X[700:]\n",
    "testY = Y[700:]\n",
    "len(testX)"
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   "execution_count": 21,
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      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "dense_4 (Dense)                  (None, 12)            108         dense_input_2[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "dense_5 (Dense)                  (None, 8)             104         dense_4[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_6 (Dense)                  (None, 1)             9           dense_5[0][0]                    \n",
      "====================================================================================================\n",
      "Total params: 221\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))\n",
    "model.add(Dense(8, init='uniform', activation='relu'))\n",
    "model.add(Dense(1, init='uniform', activation='sigmoid'))\n",
    "model.summary()"
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   "execution_count": 26,
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   "source": [
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
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   "execution_count": 50,
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       "<keras.callbacks.History at 0x7fc3a2262be0>"
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     "execution_count": 50,
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   "source": [
    "model.fit(trainX, trainY, nb_epoch=150, batch_size=10, verbose=0)"
   ]
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   "execution_count": 51,
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      "\r",
      "32/68 [=============>................] - ETA: 0s"
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     "data": {
      "text/plain": [
       "'acc: 82.35%'"
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     "execution_count": 51,
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   "source": [
    "scores = model.evaluate(testX, testY)\n",
    "\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100)"
   ]
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    "predictions = model.predict(testX)\n",
    "predictions = [round(x[0]) for x in predictions]\n",
    "list(zip(testY, predictions))"
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