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Commit f85f1d7

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and example Jupyter notebook
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‎example.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using Theano backend.\n"
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]
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}
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],
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"source": [
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"from keras.models import Sequential\n",
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"from keras import layers as l\n",
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"from keras.applications.vgg16 import VGG16\n",
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"from keras_sequential_ascii import sequential_model_to_ascii_printout"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# this model makes no sense - just a visualization of this library\n",
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"model = Sequential()\n",
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"model.add(l.BatchNormalization(input_shape=(3, 32, 32)))\n",
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"model.add(l.Convolution2D(16, 3, 3, activation='relu'))\n",
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"model.add(l.Convolution2D(16, 3, 3, activation='relu'))\n",
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"model.add(l.MaxPooling2D((2, 2)))\n",
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"model.add(l.Convolution2D(16, 1, 1, activation='tanh'))\n",
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"model.add(l.Flatten())\n",
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"model.add(l.Dense(16))\n",
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"model.add(l.Dropout(0.5))\n",
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"model.add(l.Dense(3, activation='softmax'))\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" OPERATION DATA DIMENSIONS WEIGHTS(N) WEIGHTS(%)\n",
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"\n",
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" Input ##### 3 32 32\n",
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" BatchNormalization μ|σ ------------------- 64 0.1%\n",
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" ##### 3 32 32\n",
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" Convolution2D \\|/ ------------------- 448 0.8%\n",
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" relu ##### 16 30 30\n",
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" Convolution2D \\|/ ------------------- 2320 4.4%\n",
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" relu ##### 16 28 28\n",
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" MaxPooling2D YYYYY ------------------- 0 0.0%\n",
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" ##### 16 14 14\n",
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" Convolution2D \\|/ ------------------- 272 0.5%\n",
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" tanh ##### 16 14 14\n",
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" Flatten ||||| ------------------- 0 0.0%\n",
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" ##### 3136\n",
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" Dense XXXXX ------------------- 50192 94.1%\n",
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" ##### 16\n",
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" Dropout | || ------------------- 0 0.0%\n",
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" ##### 16\n",
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" Dense XXXXX ------------------- 34 0.1%\n",
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" ##### 2\n"
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]
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}
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],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" OPERATION DATA DIMENSIONS WEIGHTS(N) WEIGHTS(%)\n",
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"\n",
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" Input ##### 3 224 224\n",
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" InputLayer ????? ------------------- 0 0.0%\n",
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" ##### 3 224 224\n",
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" Convolution2D \\|/ ------------------- 1792 0.0%\n",
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" relu ##### 64 224 224\n",
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" Convolution2D \\|/ ------------------- 36928 0.0%\n",
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" relu ##### 64 224 224\n",
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" MaxPooling2D YYYYY ------------------- 0 0.0%\n",
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" ##### 64 112 112\n",
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" Convolution2D \\|/ ------------------- 73856 0.1%\n",
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" relu ##### 128 112 112\n",
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" Convolution2D \\|/ ------------------- 147584 0.1%\n",
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" relu ##### 128 112 112\n",
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" MaxPooling2D YYYYY ------------------- 0 0.0%\n",
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" ##### 128 56 56\n",
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" Convolution2D \\|/ ------------------- 295168 0.2%\n",
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" relu ##### 256 56 56\n",
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" Convolution2D \\|/ ------------------- 590080 0.4%\n",
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" relu ##### 256 56 56\n",
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" Convolution2D \\|/ ------------------- 590080 0.4%\n",
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" relu ##### 256 56 56\n",
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" MaxPooling2D YYYYY ------------------- 0 0.0%\n",
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" ##### 256 28 28\n",
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" Convolution2D \\|/ ------------------- 1180160 0.9%\n",
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" relu ##### 512 28 28\n",
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" Convolution2D \\|/ ------------------- 2359808 1.7%\n",
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" relu ##### 512 28 28\n",
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" Convolution2D \\|/ ------------------- 2359808 1.7%\n",
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" relu ##### 512 28 28\n",
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" MaxPooling2D YYYYY ------------------- 0 0.0%\n",
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" ##### 512 14 14\n",
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" Convolution2D \\|/ ------------------- 2359808 1.7%\n",
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" relu ##### 512 14 14\n",
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" Convolution2D \\|/ ------------------- 2359808 1.7%\n",
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" relu ##### 512 14 14\n",
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" Convolution2D \\|/ ------------------- 2359808 1.7%\n",
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" relu ##### 512 14 14\n",
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" MaxPooling2D YYYYY ------------------- 0 0.0%\n",
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" ##### 512 7 7\n",
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" Flatten ||||| ------------------- 0 0.0%\n",
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" ##### 25088\n",
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" Dense XXXXX ------------------- 102764544 74.3%\n",
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" relu ##### 4096\n",
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" Dense XXXXX ------------------- 16781312 12.1%\n",
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" relu ##### 4096\n",
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" Dense XXXXX ------------------- 4097000 3.0%\n",
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" softmax ##### 1000\n"
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]
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}
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],
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"source": [
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"vgg16 = VGG16(weights=None)\n",
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"sequential_model_to_ascii_printout(vgg16)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'z' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-6-d46262532051>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m: name 'z' is not defined"
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]
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}
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],
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"source": [
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"len(z)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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