Keras_example_NN.ipynb

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    "# Create first network with Keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, SimpleRNN, Dropout\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "import numpy"
   ]
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    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "numpy.random.seed(seed)\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]"
   ]
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    "# create model\n",
    "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",
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
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    "# Fit the model\n",
    "model.fit(X, Y, nb_epoch=10, batch_size=10)\n",
    "# evaluate the model\n",
    "scores = model.evaluate(X, Y)\n",
    "print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))"
   ]
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