Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- {
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "import hyperas\n",
- "import hyperas.distributions\n",
- "import hyperopt\n",
- "import keras.layers\n",
- "import keras.models\n",
- "import keras.optimizers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "shape = (1, 128, 128)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "x = keras.layers.Input(shape)\n",
- "\n",
- "y = keras.layers.Convolution2D(64, 3, 3, activation=\"relu\", border_mode=\"same\")(x)\n",
- "y = keras.layers.Convolution2D(64, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "\n",
- "y = keras.layers.MaxPooling2D((2, 2), (2, 2))(y)\n",
- "\n",
- "y = keras.layers.Convolution2D(128, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "y = keras.layers.Convolution2D(128, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "\n",
- "y = keras.layers.MaxPooling2D((2, 2), (2, 2))(y)\n",
- "\n",
- "y = keras.layers.Convolution2D(256, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "y = keras.layers.Convolution2D(256, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "y = keras.layers.Convolution2D(256, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "y = keras.layers.Convolution2D(256, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "\n",
- "y = keras.layers.MaxPooling2D((2, 2), (2, 2))(y)\n",
- "\n",
- "y = keras.layers.Convolution2D(512, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "y = keras.layers.Convolution2D(512, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "y = keras.layers.Convolution2D(512, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "y = keras.layers.Convolution2D(512, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "\n",
- "y = keras.layers.MaxPooling2D((2, 2), (2, 2))(y)\n",
- "\n",
- "y = keras.layers.Convolution2D(512, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "y = keras.layers.Convolution2D(512, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "y = keras.layers.Convolution2D(512, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "y = keras.layers.Convolution2D(512, 3, 3, activation=\"relu\", border_mode=\"same\")(y)\n",
- "\n",
- "# y = keras.layers.Convolution2D(4096, 8, 8, activation=\"relu\", border_mode=\"same\")(y)\n",
- "# y = keras.layers.Convolution2D(4096, 1, 1, activation=\"relu\", border_mode=\"same\")(y)\n",
- "\n",
- "# y = keras.layers.Deconvolution2D(512, 8, 8, (1, 512, 8, 8), subsample=(2, 2))(y)\n",
- "\n",
- "# y = keras.layers.UpSampling2D()(y)\n",
- "\n",
- "# y = keras.layers.Deconvolution2D(512, 3, 3, (1, 512, 16, 16), border_mode=\"same\")(y)\n",
- "# y = keras.layers.Deconvolution2D(512, 3, 3, (1, 512, 16, 16), border_mode=\"same\")(y)\n",
- "# y = keras.layers.Deconvolution2D(512, 3, 3, (1, 512, 16, 16), border_mode=\"same\")(y)\n",
- "\n",
- "# y = keras.layers.UpSampling2D()(y)\n",
- "\n",
- "# y = keras.layers.Deconvolution2D(512, 3, 3, (1, 512, 16, 16), border_mode=\"same\")(y)\n",
- "# y = keras.layers.Deconvolution2D(512, 3, 3, (1, 512, 16, 16), border_mode=\"same\")(y)\n",
- "# y = keras.layers.Deconvolution2D(512, 3, 3, (1, 512, 16, 16), border_mode=\"same\")(y)\n",
- "\n",
- "# y = keras.layers.UpSampling2D()(y)\n",
- "\n",
- "# y = keras.layers.Deconvolution2D(512, 3, 3, (1, 512, 16, 16), border_mode=\"same\")(y)\n",
- "# y = keras.layers.Deconvolution2D(512, 3, 3, (1, 512, 16, 16), border_mode=\"same\")(y)\n",
- "# y = keras.layers.Deconvolution2D(256, 3, 3, (1, 256, 16, 16), border_mode=\"same\")(y)\n",
- "\n",
- "# y = keras.layers.UpSampling2D()(y)\n",
- "\n",
- "# y = keras.layers.Deconvolution2D(256, 3, 3, (1, 256, 16, 16), border_mode=\"same\")(y)\n",
- "# y = keras.layers.Deconvolution2D(256, 3, 3, (1, 256, 16, 16), border_mode=\"same\")(y)\n",
- "# y = keras.layers.Deconvolution2D(128, 3, 3, (1, 128, 16, 16), border_mode=\"same\")(y)\n",
- "\n",
- "# y = keras.layers.UpSampling2D()(y)\n",
- "\n",
- "# y = keras.layers.Deconvolution2D(128, 3, 3, (1, 128, 16, 16), border_mode=\"same\")(y)\n",
- "# y = keras.layers.Deconvolution2D( 64, 3, 3, (1, 64, 16, 16), border_mode=\"same\")(y)\n",
- "\n",
- "# y = keras.layers.UpSampling2D()(y)\n",
- "\n",
- "# y = keras.layers.Deconvolution2D(64, 3, 3, (1, 64, 16, 16), border_mode=\"same\")(y)\n",
- "# y = keras.layers.Deconvolution2D(64, 3, 3, (1, 64, 16, 16), border_mode=\"same\")(y)\n",
- "\n",
- "# y = keras.layers.Convolution2D(3, 1, 1, activation=\"relu\")\n",
- "\n",
- "model = keras.models.Model(x, y)"
- ]
- },
- {
- "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": 2
- }
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement