keras-multi-output.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "from keras.models import Model\n",
    "from keras.layers import Layer, Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiOutputLayer(Layer):\n",
    "    def get_output_shape_for(self, input_shape):\n",
    "        return [input_shape, input_shape]\n",
    "    \n",
    "    def compute_mask(self, inputs, masks):\n",
    "        return [None, None]\n",
    "\n",
    "    def call(self, x, mask=None):\n",
    "        return [x + 1, x - 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "input = Input(shape=(10,))\n",
    "parts = MultiOutputLayer(name='2-output')(input)\n",
    "model = Model(name='foo', input=[input], output=parts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.]], dtype=float32),\n",
       " array([[-1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.]], dtype=float32)]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([np.arange(10).reshape((1, 10))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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