keras-js/notebooks/core/RepeatVector.ipynb

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      "Using TensorFlow backend.\n"
     ]
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   "source": [
    "import numpy as np\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers.core import RepeatVector\n",
    "from keras import backend as K"
   ]
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    "def format_decimal(arr, places=6):\n",
    "    return [round(x * 10**places) / 10**places for x in arr]"
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   "source": [
    "### RepeatVector"
   ]
  },
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   "source": [
    "**[core.RepeatVector.0] repeat vector, shape [6] -> [7, 6]**"
   ]
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     "text": [
      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
      "in shape: (6,)\n",
      "out shape: (7, 6)\n",
      "out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0, 0.0, 0.2, 0.5, -0.1, 1.0, 2.0]\n"
     ]
    }
   ],
   "source": [
    "layer_0 = Input(shape=(6,))\n",
    "layer_1 = RepeatVector(7)(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
    "data_in_shape = (6,)\n",
    "print('in:', data_in)\n",
    "print('in shape:', data_in_shape)\n",
    "arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
    "result = model.predict(np.array([arr_in]))\n",
    "arr_out = result[0]\n",
    "print('out shape:', arr_out.shape)\n",
    "data_out = format_decimal(arr_out.ravel().tolist())\n",
    "print('out:', data_out)"
   ]
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