keras-js/notebooks/convolutional/Cropping1D.ipynb

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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.convolutional import Cropping1D\n",
    "from keras import backend as K"
   ]
  },
  {
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   "execution_count": 11,
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   "source": [
    "def format_decimal(arr, places=6):\n",
    "    return [round(x * 10**places) / 10**places for x in arr]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cropping1D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[convolutional.Cropping1D.0] cropping (1,1) on 6x4 input**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
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     "text": [
      "\n",
      "in shape: (6, 4)\n",
      "in: [-0.412987, -0.139246, 0.567466, -0.512174, -0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487, 0.752844, 0.218213, 0.37408, 0.09404, -0.786108, -0.1961, -0.031679, -0.188902, 0.846634]\n",
      "out shape: (4, 4)\n",
      "out: [-0.33603, 0.585584, 0.453472, 0.942013, -0.609538, -0.894302, 0.711927, -0.126057, 0.677549, 0.676991, 0.471487, 0.752844, 0.218213, 0.37408, 0.09404, -0.786108]\n"
     ]
    }
   ],
   "source": [
    "data_in_shape = (6, 4)\n",
    "L = Cropping1D(cropping=(1,1))\n",
    "\n",
    "layer_0 = Input(shape=data_in_shape)\n",
    "layer_1 = L(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "# set weights to random (use seed for reproducibility)\n",
    "np.random.seed(240)\n",
    "data_in = 2 * np.random.random(data_in_shape) - 1\n",
    "print('')\n",
    "print('in shape:', data_in_shape)\n",
    "print('in:', format_decimal(data_in.ravel().tolist()))\n",
    "result = model.predict(np.array([data_in]))\n",
    "print('out shape:', result[0].shape)\n",
    "print('out:', format_decimal(result[0].ravel().tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[convolutional.Cropping1D.1] cropping (2,3) on 6x4 input**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-11-15T10:19:26.401100",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "in shape: (6, 4)\n",
      "in: [0.229895, 0.156613, -0.66952, -0.996975, 0.45773, 0.684298, -0.999213, 0.276751, -0.484373, -0.506163, 0.353904, 0.513668, -0.981594, 0.78914, 0.603978, 0.204066, 0.532874, 0.866766, -0.544852, 0.876693, -0.523052, -0.716556, 0.875262, 0.544879]\n",
      "out shape: (1, 4)\n",
      "out: [-0.484373, -0.506163, 0.353904, 0.513668]\n"
     ]
    }
   ],
   "source": [
    "data_in_shape = (6, 4)\n",
    "L = Cropping1D(cropping=(2,3))\n",
    "\n",
    "layer_0 = Input(shape=data_in_shape)\n",
    "layer_1 = L(layer_0)\n",
    "model = Model(input=layer_0, output=layer_1)\n",
    "\n",
    "# set weights to random (use seed for reproducibility)\n",
    "np.random.seed(241)\n",
    "data_in = 2 * np.random.random(data_in_shape) - 1\n",
    "print('')\n",
    "print('in shape:', data_in_shape)\n",
    "print('in:', format_decimal(data_in.ravel().tolist()))\n",
    "result = model.predict(np.array([data_in]))\n",
    "print('out shape:', result[0].shape)\n",
    "print('out:', format_decimal(result[0].ravel().tolist()))"
   ]
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