diff --git a/notebooks/core.ipynb b/notebooks/core.ipynb
deleted file mode 100644
index 48ad92b..0000000
--- a/notebooks/core.ipynb
+++ /dev/null
@@ -1,1600 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from keras.models import Model\n",
-    "from keras.engine import merge\n",
-    "from keras.layers import Input\n",
-    "from keras.layers.core import Dense, Activation, Dropout\n",
-    "from keras.layers.core import Flatten, Reshape, Permute, RepeatVector\n",
-    "from keras.layers.core import Merge, Lambda, ActivityRegularization, Masking\n",
-    "from keras.layers.core import Highway, MaxoutDense, TimeDistributedDense\n",
-    "from keras import backend as K"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def format_decimal(arr, places=6):\n",
-    "    return [round(x * 10**places) / 10**places for x in arr]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dense"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Dense.0] test 1**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [7.3, -0.21]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1 = Dense(2, activation='linear')(layer_0)\n",
-    "model = Model(input=layer_0, output=layer_1)\n",
-    "\n",
-    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b = np.array([0.5, 0.7])\n",
-    "model.set_weights([W, b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Dense.1] test 2 (with sigmoid activation)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [0.999325, 0.447692]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1 = Dense(2, activation='sigmoid')(layer_0)\n",
-    "model = Model(input=layer_0, output=layer_1)\n",
-    "\n",
-    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b = np.array([0.5, 0.7])\n",
-    "model.set_weights([W, b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Dense.2] test 3 (with softplus activation and no bias)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [6.801113, 0.338274]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1 = Dense(2, activation='softplus', bias=False)(layer_0)\n",
-    "model = Model(input=layer_0, output=layer_1)\n",
-    "\n",
-    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "model.set_weights([W])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Dense.3] [GPU] test 1**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [7.3, -0.21]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1 = Dense(2, activation='linear')(layer_0)\n",
-    "model = Model(input=layer_0, output=layer_1)\n",
-    "\n",
-    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b = np.array([0.5, 0.7])\n",
-    "model.set_weights([W, b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Dense.4] [GPU] test 2 (with sigmoid activation)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [0.999325, 0.447692]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1 = Dense(2, activation='sigmoid')(layer_0)\n",
-    "model = Model(input=layer_0, output=layer_1)\n",
-    "\n",
-    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b = np.array([0.5, 0.7])\n",
-    "model.set_weights([W, b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Dense.5] [GPU] test 3 (with softplus activation and no bias)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [6.801113, 0.338274]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1 = Dense(2, activation='softplus', bias=False)(layer_0)\n",
-    "model = Model(input=layer_0, output=layer_1)\n",
-    "\n",
-    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "model.set_weights([W])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Activation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Activation.0] test 1 (tanh)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [0.999999, -0.206966]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1 = Dense(2)(layer_0)\n",
-    "layer_2 = Activation('tanh')(layer_1)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b = np.array([0.5, 0.7])\n",
-    "model.set_weights([W, b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Activation.1] test 2 (hardSigmoid)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [1.0, 0.458]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1 = Dense(2)(layer_0)\n",
-    "layer_2 = Activation('hard_sigmoid')(layer_1)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b = np.array([0.5, 0.7])\n",
-    "model.set_weights([W, b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dropout"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Dropout.0] should pass through**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [7.3, -0.21]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1 = Dense(2)(layer_0)\n",
-    "layer_2 = Dropout(0.5)(layer_1)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b = np.array([0.5, 0.7])\n",
-    "model.set_weights([W, b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Flatten"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Flatten.0] 1D**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# layer_0 = Input(shape=(6,))\n",
-    "# layer_1 = Flatten()(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)\n",
-    "\n",
-    "# exception with TF"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Flatten.1] 2D**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (3, 2)\n",
-      "out shape: (6,)\n",
-      "out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(3, 2))\n",
-    "layer_1 = Flatten()(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 = (3, 2)\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Flatten.2] 3D**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (3, 2, 2)\n",
-      "out shape: (12,)\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]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(3, 2, 2))\n",
-    "layer_1 = Flatten()(layer_0)\n",
-    "model = Model(input=layer_0, output=layer_1)\n",
-    "\n",
-    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
-    "data_in_shape = (3, 2, 2)\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Reshape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Reshape.0] shape [6] -> [2, 3]**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2, 3)\n",
-      "out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1 = Reshape((2, 3))(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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Reshape.1] shape [3, 2] -> [6]**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (3, 2)\n",
-      "out shape: (6,)\n",
-      "out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(3, 2))\n",
-    "layer_1 = Reshape((6,))(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 = (3, 2)\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Reshape.2] shape [3, 2, 2] -> [4, 3]**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (3, 2, 2)\n",
-      "out shape: (4, 3)\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]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(3, 2, 2))\n",
-    "layer_1 = Reshape((4, 3))(layer_0)\n",
-    "model = Model(input=layer_0, output=layer_1)\n",
-    "\n",
-    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
-    "data_in_shape = (3, 2, 2)\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Permute"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Permute.0] shape [3, 2] -> [2, 3]**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (3, 2)\n",
-      "out shape: (2, 3)\n",
-      "out: [0.0, 0.5, 1.0, 0.2, -0.1, 2.0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(3, 2))\n",
-    "layer_1 = Permute((2, 1))(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 = (3, 2)\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Permute.1] shape [2, 3, 4] -> [4, 3, 2]**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (2, 3, 4)\n",
-      "out shape: (4, 3, 2)\n",
-      "out: [0.0, 0.0, 1.0, 1.0, 0.5, 0.5, 0.2, 0.2, 2.0, 2.0, -0.1, -0.1, 0.5, 0.5, 0.0, 0.0, 1.0, 1.0, -0.1, -0.1, 0.2, 0.2, 2.0, 2.0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(2, 3, 4))\n",
-    "layer_1 = Permute((3, 2, 1))(layer_0)\n",
-    "model = Model(input=layer_0, output=layer_1)\n",
-    "\n",
-    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
-    "data_in_shape = (2, 3, 4)\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### RepeatVector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.RepeatVector.0] repeat vector, shape [6] -> [7, 6]**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Merge"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.0] mode: sum**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [4.85, 4.27]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='sum')\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.1] mode: mul**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [-17.885, -0.9408]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='mul')\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.2] mode: ave**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [2.425, 2.135]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='ave')\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.3] mode: max**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2,)\n",
-      "out: [7.3, 4.48]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='max')\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.4] mode: concat (1D)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (4,)\n",
-      "out: [7.3, -0.21, -2.45, 4.48]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='concat', concat_axis=-1)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.5] mode: concat (2D, concatAxis=-1)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (3, 4)\n",
-      "out: [7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1a = RepeatVector(3)(layer_1a)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = RepeatVector(3)(layer_1b)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='concat', concat_axis=-1)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.6] mode: concat (2D, concatAxis=-2)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (6, 2)\n",
-      "out: [7.3, -0.21, 7.3, -0.21, 7.3, -0.21, -2.45, 4.48, -2.45, 4.48, -2.45, 4.48]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1a = RepeatVector(3)(layer_1a)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = RepeatVector(3)(layer_1b)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='concat', concat_axis=-2)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.7] mode: concat (2D, concatAxis=1)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (6, 2)\n",
-      "out: [7.3, -0.21, 7.3, -0.21, 7.3, -0.21, -2.45, 4.48, -2.45, 4.48, -2.45, 4.48]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1a = RepeatVector(3)(layer_1a)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = RepeatVector(3)(layer_1b)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='concat', concat_axis=1)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.8] mode: concat (2D, concatAxis=2)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (3, 4)\n",
-      "out: [7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1a = RepeatVector(3)(layer_1a)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = RepeatVector(3)(layer_1b)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='concat', concat_axis=2)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.9] mode: dot (2D x 2D, dotAxes=1)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (2, 2)\n",
-      "out: [-53.655003, 98.112007, 1.5435, -2.8224]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1a = RepeatVector(3)(layer_1a)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = RepeatVector(3)(layer_1b)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='dot', dot_axes=1)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.10] mode: dot (2D x 2D, dotAxes=2)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (3, 3)\n",
-      "out: [-18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1a = RepeatVector(3)(layer_1a)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = RepeatVector(3)(layer_1b)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='dot', dot_axes=2)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.11] mode: cos (2D x 2D, dotAxes=1)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (1, 2, 2)\n",
-      "out: [-1.0, 7.972744, 0.125427, -1.0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1a = RepeatVector(3)(layer_1a)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = RepeatVector(3)(layer_1b)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='cos', dot_axes=1)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**[core.Merge.12] mode: cos (2D x 2D, dotAxes=2)**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
-      "in shape: (6,)\n",
-      "out shape: (1, 3, 3)\n",
-      "out: [-0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843]\n"
-     ]
-    }
-   ],
-   "source": [
-    "layer_0 = Input(shape=(6,))\n",
-    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1a = RepeatVector(3)(layer_1a)\n",
-    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
-    "layer_1b = RepeatVector(3)(layer_1b)\n",
-    "layer_2 = merge([layer_1a, layer_1b], mode='cos', dot_axes=2)\n",
-    "model = Model(input=layer_0, output=layer_2)\n",
-    "\n",
-    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
-    "b_1a = np.array([0.5, 0.7])\n",
-    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
-    "b_1b = np.array([0.1, -0.2])\n",
-    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Highway"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### MaxoutDense"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### TimeDistributedDense"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/core/Activation.ipynb b/notebooks/core/Activation.ipynb
new file mode 100644
index 0000000..67a5c26
--- /dev/null
+++ b/notebooks/core/Activation.ipynb
@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Model\n",
+    "from keras.layers import Input\n",
+    "from keras.layers.core import Dense, Activation\n",
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def format_decimal(arr, places=6):\n",
+    "    return [round(x * 10**places) / 10**places for x in arr]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Activation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Activation.0] test 1 (tanh)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [0.999999, -0.206966]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1 = Dense(2)(layer_0)\n",
+    "layer_2 = Activation('tanh')(layer_1)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b = np.array([0.5, 0.7])\n",
+    "model.set_weights([W, b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Activation.1] test 2 (hardSigmoid)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [1.0, 0.458]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1 = Dense(2)(layer_0)\n",
+    "layer_2 = Activation('hard_sigmoid')(layer_1)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b = np.array([0.5, 0.7])\n",
+    "model.set_weights([W, b])\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/core/Dense.ipynb b/notebooks/core/Dense.ipynb
new file mode 100644
index 0000000..8eb1934
--- /dev/null
+++ b/notebooks/core/Dense.ipynb
@@ -0,0 +1,350 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Model\n",
+    "from keras.layers import Input\n",
+    "from keras.layers.core import Dense\n",
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def format_decimal(arr, places=6):\n",
+    "    return [round(x * 10**places) / 10**places for x in arr]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dense"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Dense.0] test 1**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [7.3, -0.21]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1 = Dense(2, activation='linear')(layer_0)\n",
+    "model = Model(input=layer_0, output=layer_1)\n",
+    "\n",
+    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b = np.array([0.5, 0.7])\n",
+    "model.set_weights([W, b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Dense.1] test 2 (with sigmoid activation)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [0.999325, 0.447692]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1 = Dense(2, activation='sigmoid')(layer_0)\n",
+    "model = Model(input=layer_0, output=layer_1)\n",
+    "\n",
+    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b = np.array([0.5, 0.7])\n",
+    "model.set_weights([W, b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Dense.2] test 3 (with softplus activation and no bias)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [6.801113, 0.338274]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1 = Dense(2, activation='softplus', bias=False)(layer_0)\n",
+    "model = Model(input=layer_0, output=layer_1)\n",
+    "\n",
+    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "model.set_weights([W])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Dense.3] [GPU] test 1**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [7.3, -0.21]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1 = Dense(2, activation='linear')(layer_0)\n",
+    "model = Model(input=layer_0, output=layer_1)\n",
+    "\n",
+    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b = np.array([0.5, 0.7])\n",
+    "model.set_weights([W, b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Dense.4] [GPU] test 2 (with sigmoid activation)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [0.999325, 0.447692]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1 = Dense(2, activation='sigmoid')(layer_0)\n",
+    "model = Model(input=layer_0, output=layer_1)\n",
+    "\n",
+    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b = np.array([0.5, 0.7])\n",
+    "model.set_weights([W, b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Dense.5] [GPU] test 3 (with softplus activation and no bias)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [6.801113, 0.338274]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1 = Dense(2, activation='softplus', bias=False)(layer_0)\n",
+    "model = Model(input=layer_0, output=layer_1)\n",
+    "\n",
+    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "model.set_weights([W])\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/core/Dropout.ipynb b/notebooks/core/Dropout.ipynb
new file mode 100644
index 0000000..5b003b6
--- /dev/null
+++ b/notebooks/core/Dropout.ipynb
@@ -0,0 +1,123 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Model\n",
+    "from keras.layers import Input\n",
+    "from keras.layers.core import Dense, Dropout\n",
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def format_decimal(arr, places=6):\n",
+    "    return [round(x * 10**places) / 10**places for x in arr]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dropout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Dropout.0] should pass through**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [7.3, -0.21]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1 = Dense(2)(layer_0)\n",
+    "layer_2 = Dropout(0.5)(layer_1)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b = np.array([0.5, 0.7])\n",
+    "model.set_weights([W, b])\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/core/Flatten.ipynb b/notebooks/core/Flatten.ipynb
new file mode 100644
index 0000000..79a6d00
--- /dev/null
+++ b/notebooks/core/Flatten.ipynb
@@ -0,0 +1,193 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Model\n",
+    "from keras.layers import Input\n",
+    "from keras.layers.core import Flatten\n",
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def format_decimal(arr, places=6):\n",
+    "    return [round(x * 10**places) / 10**places for x in arr]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Flatten"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Flatten.0] 1D**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# layer_0 = Input(shape=(6,))\n",
+    "# layer_1 = Flatten()(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)\n",
+    "\n",
+    "# exception with TF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Flatten.1] 2D**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (3, 2)\n",
+      "out shape: (6,)\n",
+      "out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(3, 2))\n",
+    "layer_1 = Flatten()(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 = (3, 2)\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Flatten.2] 3D**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (3, 2, 2)\n",
+      "out shape: (12,)\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]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(3, 2, 2))\n",
+    "layer_1 = Flatten()(layer_0)\n",
+    "model = Model(input=layer_0, output=layer_1)\n",
+    "\n",
+    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
+    "data_in_shape = (3, 2, 2)\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/core/Merge.ipynb b/notebooks/core/Merge.ipynb
new file mode 100644
index 0000000..9b7984c
--- /dev/null
+++ b/notebooks/core/Merge.ipynb
@@ -0,0 +1,743 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Model\n",
+    "from keras.engine import merge\n",
+    "from keras.layers import Input\n",
+    "from keras.layers.core import Dense, Merge\n",
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def format_decimal(arr, places=6):\n",
+    "    return [round(x * 10**places) / 10**places for x in arr]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Merge"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.0] mode: sum**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [4.85, 4.27]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='sum')\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.1] mode: mul**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [-17.885, -0.9408]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='mul')\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.2] mode: ave**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [2.425, 2.135]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='ave')\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.3] mode: max**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2,)\n",
+      "out: [7.3, 4.48]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='max')\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.4] mode: concat (1D)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (4,)\n",
+      "out: [7.3, -0.21, -2.45, 4.48]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='concat', concat_axis=-1)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.5] mode: concat (2D, concatAxis=-1)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (3, 4)\n",
+      "out: [7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1a = RepeatVector(3)(layer_1a)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = RepeatVector(3)(layer_1b)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='concat', concat_axis=-1)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.6] mode: concat (2D, concatAxis=-2)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (6, 2)\n",
+      "out: [7.3, -0.21, 7.3, -0.21, 7.3, -0.21, -2.45, 4.48, -2.45, 4.48, -2.45, 4.48]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1a = RepeatVector(3)(layer_1a)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = RepeatVector(3)(layer_1b)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='concat', concat_axis=-2)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.7] mode: concat (2D, concatAxis=1)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (6, 2)\n",
+      "out: [7.3, -0.21, 7.3, -0.21, 7.3, -0.21, -2.45, 4.48, -2.45, 4.48, -2.45, 4.48]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1a = RepeatVector(3)(layer_1a)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = RepeatVector(3)(layer_1b)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='concat', concat_axis=1)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.8] mode: concat (2D, concatAxis=2)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (3, 4)\n",
+      "out: [7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48, 7.3, -0.21, -2.45, 4.48]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1a = RepeatVector(3)(layer_1a)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = RepeatVector(3)(layer_1b)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='concat', concat_axis=2)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.9] mode: dot (2D x 2D, dotAxes=1)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2, 2)\n",
+      "out: [-53.655003, 98.112007, 1.5435, -2.8224]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1a = RepeatVector(3)(layer_1a)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = RepeatVector(3)(layer_1b)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='dot', dot_axes=1)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.10] mode: dot (2D x 2D, dotAxes=2)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (3, 3)\n",
+      "out: [-18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258, -18.8258]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1a = RepeatVector(3)(layer_1a)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = RepeatVector(3)(layer_1b)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='dot', dot_axes=2)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.11] mode: cos (2D x 2D, dotAxes=1)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (1, 2, 2)\n",
+      "out: [-1.0, 7.972744, 0.125427, -1.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1a = RepeatVector(3)(layer_1a)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = RepeatVector(3)(layer_1b)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='cos', dot_axes=1)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Merge.12] mode: cos (2D x 2D, dotAxes=2)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (1, 3, 3)\n",
+      "out: [-0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843, -0.504843]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1a = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1a = RepeatVector(3)(layer_1a)\n",
+    "layer_1b = Dense(2, activation='linear')(layer_0)\n",
+    "layer_1b = RepeatVector(3)(layer_1b)\n",
+    "layer_2 = merge([layer_1a, layer_1b], mode='cos', dot_axes=2)\n",
+    "model = Model(input=layer_0, output=layer_2)\n",
+    "\n",
+    "W_1a = np.array([0.1, 0.4, 0.5, 0.1, 1, -2, 0, 0.3, 0.2, 0.1, 3, 0]).reshape((6, 2))\n",
+    "b_1a = np.array([0.5, 0.7])\n",
+    "W_1b = np.array([1, 0, -0.9, 0.6, -0.7, 0, 0.2, 0.4, 0, 0, -1, 2.3]).reshape((6, 2))\n",
+    "b_1b = np.array([0.1, -0.2])\n",
+    "model.set_weights([W_1a, b_1a, W_1b, b_1b])\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/core/Permute.ipynb b/notebooks/core/Permute.ipynb
new file mode 100644
index 0000000..1317c39
--- /dev/null
+++ b/notebooks/core/Permute.ipynb
@@ -0,0 +1,160 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Model\n",
+    "from keras.layers import Input\n",
+    "from keras.layers.core import Permute\n",
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def format_decimal(arr, places=6):\n",
+    "    return [round(x * 10**places) / 10**places for x in arr]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Permute"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Permute.0] shape [3, 2] -> [2, 3]**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (3, 2)\n",
+      "out shape: (2, 3)\n",
+      "out: [0.0, 0.5, 1.0, 0.2, -0.1, 2.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(3, 2))\n",
+    "layer_1 = Permute((2, 1))(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 = (3, 2)\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Permute.1] shape [2, 3, 4] -> [4, 3, 2]**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (2, 3, 4)\n",
+      "out shape: (4, 3, 2)\n",
+      "out: [0.0, 0.0, 1.0, 1.0, 0.5, 0.5, 0.2, 0.2, 2.0, 2.0, -0.1, -0.1, 0.5, 0.5, 0.0, 0.0, 1.0, 1.0, -0.1, -0.1, 0.2, 0.2, 2.0, 2.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(2, 3, 4))\n",
+    "layer_1 = Permute((3, 2, 1))(layer_0)\n",
+    "model = Model(input=layer_0, output=layer_1)\n",
+    "\n",
+    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
+    "data_in_shape = (2, 3, 4)\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/core/RepeatVector.ipynb b/notebooks/core/RepeatVector.ipynb
new file mode 100644
index 0000000..b0269cf
--- /dev/null
+++ b/notebooks/core/RepeatVector.ipynb
@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def format_decimal(arr, places=6):\n",
+    "    return [round(x * 10**places) / 10**places for x in arr]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### RepeatVector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.RepeatVector.0] repeat vector, shape [6] -> [7, 6]**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/core/Reshape.ipynb b/notebooks/core/Reshape.ipynb
new file mode 100644
index 0000000..5491802
--- /dev/null
+++ b/notebooks/core/Reshape.ipynb
@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from keras.models import Model\n",
+    "from keras.layers import Input\n",
+    "from keras.layers.core import Reshape\n",
+    "from keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def format_decimal(arr, places=6):\n",
+    "    return [round(x * 10**places) / 10**places for x in arr]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reshape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Reshape.0] shape [6] -> [2, 3]**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (6,)\n",
+      "out shape: (2, 3)\n",
+      "out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(6,))\n",
+    "layer_1 = Reshape((2, 3))(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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Reshape.1] shape [3, 2] -> [6]**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (3, 2)\n",
+      "out shape: (6,)\n",
+      "out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(3, 2))\n",
+    "layer_1 = Reshape((6,))(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 = (3, 2)\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**[core.Reshape.2] shape [3, 2, 2] -> [4, 3]**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in: [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
+      "in shape: (3, 2, 2)\n",
+      "out shape: (4, 3)\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]\n"
+     ]
+    }
+   ],
+   "source": [
+    "layer_0 = Input(shape=(3, 2, 2))\n",
+    "layer_1 = Reshape((4, 3))(layer_0)\n",
+    "model = Model(input=layer_0, output=layer_1)\n",
+    "\n",
+    "data_in = [0, 0.2, 0.5, -0.1, 1, 2, 0, 0.2, 0.5, -0.1, 1, 2]\n",
+    "data_in_shape = (3, 2, 2)\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}