mirror of
https://github.com/wassname/keras-js.git
synced 2026-09-09 11:25:25 +08:00
1165 lines
29 KiB
Plaintext
1165 lines
29 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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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 TensorFlow backend.\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"from keras import activations\n",
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"from keras import backend as K"
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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": true
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},
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"outputs": [],
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"source": [
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"def format_decimal(arr):\n",
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" return [round(x * 1e6) / 1e6 for x in arr]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### softmax"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**[activations.softmax.0] 1D**"
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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": 12,
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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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"in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
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"in shape: (6,)\n",
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"out shape: (6,)\n",
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"out: [0.067194, 0.082071, 0.110784, 0.0608, 0.182652, 0.4965]\n"
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]
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}
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],
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"source": [
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"data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
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"data_in_shape = (6,)\n",
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"print('in:', data_in)\n",
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"print('in shape:', data_in_shape)\n",
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"arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
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"\n",
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"result = activations.softmax(K.variable(np.array([arr_in])))\n",
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"\n",
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"arr_out = K.eval(result)[0]\n",
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"print('out shape:', arr_out.shape)\n",
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"data_out = format_decimal(arr_out.ravel().tolist())\n",
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"print('out:', data_out)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**[activations.softmax.1] 2D**"
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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": 14,
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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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"in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
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"in shape: (2, 6)\n",
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"out shape: (2, 6)\n",
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"out: [0.067194, 0.082071, 0.110784, 0.0608, 0.182652, 0.4965, 0.107768, 0.149902, 0.11105, 0.247147, 0.082268, 0.301865]\n"
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]
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}
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],
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"source": [
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"data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
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"data_in_shape = (2, 6)\n",
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"print('in:', data_in)\n",
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"print('in shape:', data_in_shape)\n",
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"arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
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"\n",
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"result = activations.softmax(K.variable(np.array([arr_in])))\n",
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"\n",
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"arr_out = K.eval(result)[0]\n",
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"print('out shape:', arr_out.shape)\n",
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"data_out = format_decimal(arr_out.ravel().tolist())\n",
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"print('out:', data_out)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**[activations.softmax.2] 1D**"
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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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"in: [0, 0.2, 0.5, -0.1, 1, 90]\n",
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"in shape: (6,)\n",
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"out shape: (6,)\n",
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"out: [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]\n"
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]
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}
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],
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"source": [
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"data_in = [0, 0.2, 0.5, -0.1, 1, 90]\n",
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"data_in_shape = (6,)\n",
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"print('in:', data_in)\n",
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"print('in shape:', data_in_shape)\n",
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"arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
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"\n",
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"result = activations.softmax(K.variable(np.array([arr_in])))\n",
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"\n",
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"arr_out = K.eval(result)[0]\n",
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"print('out shape:', arr_out.shape)\n",
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"data_out = format_decimal(arr_out.ravel().tolist())\n",
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"print('out:', data_out)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### softplus"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**[activations.softplus.0] 1D**"
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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": 25,
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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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"in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
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"in shape: (6,)\n",
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"out shape: (6,)\n",
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"out: [0.693147, 0.798139, 0.974077, 0.644397, 1.313262, 2.126928]\n"
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]
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}
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|
],
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"source": [
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"data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
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"data_in_shape = (6,)\n",
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"print('in:', data_in)\n",
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"print('in shape:', data_in_shape)\n",
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"arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
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"\n",
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"result = activations.softplus(K.variable(np.array([arr_in])))\n",
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"\n",
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"arr_out = K.eval(result)[0]\n",
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"print('out shape:', arr_out.shape)\n",
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"data_out = format_decimal(arr_out.ravel().tolist())\n",
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"print('out:', data_out)"
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]
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},
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|
{
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"cell_type": "markdown",
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"metadata": {},
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|
"source": [
|
|
"**[activations.softplus.1] 2D**"
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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": 26,
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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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"in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
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"in shape: (2, 6)\n",
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"out shape: (2, 6)\n",
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"out: [0.693147, 0.798139, 0.974077, 0.644397, 1.313262, 2.126928, 0.67826, 0.854355, 0.693147, 1.171101, 0.554355, 1.313262]\n"
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|
]
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|
}
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|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
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"data_in_shape = (2, 6)\n",
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"print('in:', data_in)\n",
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"print('in shape:', data_in_shape)\n",
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"arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
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"\n",
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"result = activations.softplus(K.variable(np.array([arr_in])))\n",
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"\n",
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"arr_out = K.eval(result)[0]\n",
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"print('out shape:', arr_out.shape)\n",
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"data_out = format_decimal(arr_out.ravel().tolist())\n",
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"print('out:', data_out)"
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]
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},
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|
{
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"cell_type": "markdown",
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|
"metadata": {},
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|
"source": [
|
|
"**[activations.softplus.2] 3D**"
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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": 27,
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|
"metadata": {
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|
"collapsed": false
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|
},
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|
"outputs": [
|
|
{
|
|
"name": "stdout",
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|
"output_type": "stream",
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|
"text": [
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|
"in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
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|
"in shape: (2, 2, 3)\n",
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"out shape: (2, 2, 3)\n",
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"out: [0.693147, 0.798139, 0.474077, 0.644397, 1.313262, 2.126928, 0.67826, 2.395545, 0.693147, 1.171101, 0.554355, 1.313262]\n"
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|
]
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|
}
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|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
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"data_in_shape = (2, 2, 3)\n",
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|
"print('in:', data_in)\n",
|
|
"print('in shape:', data_in_shape)\n",
|
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"arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
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"\n",
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|
"result = activations.softplus(K.variable(np.array([arr_in])))\n",
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"\n",
|
|
"arr_out = K.eval(result)[0]\n",
|
|
"print('out shape:', arr_out.shape)\n",
|
|
"data_out = format_decimal(arr_out.ravel().tolist())\n",
|
|
"print('out:', data_out)"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
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|
"metadata": {},
|
|
"source": [
|
|
"### softsign"
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|
]
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|
},
|
|
{
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|
"cell_type": "markdown",
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|
"metadata": {},
|
|
"source": [
|
|
"**[activations.softsign.0] 1D**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {
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|
"collapsed": false
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|
},
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|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
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|
"text": [
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|
"in: [0, 0.2, 0.5, -0.1, 1, 2]\n",
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"in shape: (6,)\n",
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"out shape: (6,)\n",
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"out: [0.0, 0.166667, 0.333333, -0.090909, 0.5, 0.666667]\n"
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|
]
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|
}
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|
],
|
|
"source": [
|
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"data_in = [0, 0.2, 0.5, -0.1, 1, 2]\n",
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"data_in_shape = (6,)\n",
|
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"print('in:', data_in)\n",
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|
"print('in shape:', data_in_shape)\n",
|
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"arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
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"\n",
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"result = activations.softsign(K.variable(np.array([arr_in])))\n",
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"\n",
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"arr_out = K.eval(result)[0]\n",
|
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"print('out shape:', arr_out.shape)\n",
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"data_out = format_decimal(arr_out.ravel().tolist())\n",
|
|
"print('out:', data_out)"
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]
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|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**[activations.softsign.1] 2D**"
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|
]
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|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
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|
"text": [
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|
"in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
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"in shape: (2, 6)\n",
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"out shape: (2, 6)\n",
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"out: [0.0, 0.166667, 0.333333, -0.090909, 0.5, 0.666667, -0.029126, 0.230769, 0.0, 0.444444, -0.230769, 0.5]\n"
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|
]
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|
}
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|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
|
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"data_in_shape = (2, 6)\n",
|
|
"print('in:', data_in)\n",
|
|
"print('in shape:', data_in_shape)\n",
|
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"arr_in = np.array(data_in, dtype='float32').reshape(data_in_shape)\n",
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"\n",
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"result = activations.softsign(K.variable(np.array([arr_in])))\n",
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"\n",
|
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"arr_out = K.eval(result)[0]\n",
|
|
"print('out shape:', arr_out.shape)\n",
|
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"data_out = format_decimal(arr_out.ravel().tolist())\n",
|
|
"print('out:', data_out)"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**[activations.softsign.2] 3D**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 2, 3)\n",
|
|
"out shape: (2, 2, 3)\n",
|
|
"out: [0.0, 0.166667, -0.333333, -0.090909, 0.5, 0.666667, -0.029126, 0.69697, 0.0, 0.444444, -0.230769, 0.5]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 2, 3)\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",
|
|
"\n",
|
|
"result = activations.softsign(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"### ReLU"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**[activations.relu.0] 1D**"
|
|
]
|
|
},
|
|
{
|
|
"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: (6,)\n",
|
|
"out: [0.0, 0.2, 0.5, 0.0, 1.0, 2.0]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"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",
|
|
"\n",
|
|
"result = activations.relu(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.relu.1] 2D**"
|
|
]
|
|
},
|
|
{
|
|
"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, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 6)\n",
|
|
"out shape: (2, 6)\n",
|
|
"out: [0.0, 0.2, 0.5, 0.0, 1.0, 2.0, 0.0, 0.3, 0.0, 0.8, 0.0, 1.0]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 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",
|
|
"\n",
|
|
"result = activations.relu(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.relu.2] 3D**"
|
|
]
|
|
},
|
|
{
|
|
"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, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 2, 3)\n",
|
|
"out shape: (2, 2, 3)\n",
|
|
"out: [0.0, 0.2, 0.0, 0.0, 1.0, 2.0, 0.0, 2.3, 0.0, 0.8, 0.0, 1.0]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 2, 3)\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",
|
|
"\n",
|
|
"result = activations.relu(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.relu.3] 3D, max_value=0.5**"
|
|
]
|
|
},
|
|
{
|
|
"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, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 2, 3)\n",
|
|
"out shape: (2, 2, 3)\n",
|
|
"out: [0.0, 0.2, 0.0, 0.0, 0.5, 0.5, 0.0, 0.5, 0.0, 0.5, 0.0, 0.5]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 2, 3)\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",
|
|
"\n",
|
|
"result = activations.relu(K.variable(np.array([arr_in])), max_value=0.5)\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.relu.4] 3D, alpha=0.3**"
|
|
]
|
|
},
|
|
{
|
|
"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, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 2, 3)\n",
|
|
"out shape: (2, 2, 3)\n",
|
|
"out: [0.0, 0.2, -0.15, -0.03, 1.0, 2.0, -0.009, 2.3, 0.0, 0.8, -0.09, 1.0]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 2, 3)\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",
|
|
"\n",
|
|
"result = activations.relu(K.variable(np.array([arr_in])), alpha=0.3)\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"### tanh"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**[activations.tanh.0] 1D**"
|
|
]
|
|
},
|
|
{
|
|
"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: (6,)\n",
|
|
"out: [0.0, 0.197375, 0.462117, -0.099668, 0.761594, 0.964028]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"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",
|
|
"\n",
|
|
"result = activations.tanh(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.tanh.1] 2D**"
|
|
]
|
|
},
|
|
{
|
|
"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, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 6)\n",
|
|
"out shape: (2, 6)\n",
|
|
"out: [0.0, 0.197375, 0.462117, -0.099668, 0.761594, 0.964028, -0.029991, 0.291313, 0.0, 0.664037, -0.291313, 0.761594]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 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",
|
|
"\n",
|
|
"result = activations.tanh(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.tanh.2] 3D**"
|
|
]
|
|
},
|
|
{
|
|
"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, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 2, 3)\n",
|
|
"out shape: (2, 2, 3)\n",
|
|
"out: [0.0, 0.197375, -0.462117, -0.099668, 0.761594, 0.964028, -0.029991, 0.980096, 0.0, 0.664037, -0.291313, 0.761594]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 2, 3)\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",
|
|
"\n",
|
|
"result = activations.tanh(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"### sigmoid"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**[activations.sigmoid.0] 1D**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"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,)\n",
|
|
"out: [0.5, 0.549834, 0.622459, 0.475021, 0.731059, 0.880797]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"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",
|
|
"\n",
|
|
"result = activations.sigmoid(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.sigmoid.1] 2D**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 6)\n",
|
|
"out shape: (2, 6)\n",
|
|
"out: [0.5, 0.549834, 0.622459, 0.475021, 0.731059, 0.880797, 0.492501, 0.574443, 0.5, 0.689974, 0.425557, 0.731059]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 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",
|
|
"\n",
|
|
"result = activations.sigmoid(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.sigmoid.2] 3D**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 2, 3)\n",
|
|
"out shape: (2, 2, 3)\n",
|
|
"out: [0.5, 0.549834, 0.377541, 0.475021, 0.731059, 0.880797, 0.492501, 0.908877, 0.5, 0.689974, 0.425557, 0.731059]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 2, 3)\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",
|
|
"\n",
|
|
"result = activations.sigmoid(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"### hardSigmoid"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**[activations.hardSigmoid.0] 1D**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"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,)\n",
|
|
"out: [0.5, 0.54, 0.6, 0.48, 0.7, 0.9]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"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",
|
|
"\n",
|
|
"result = activations.hard_sigmoid(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.hardSigmoid.1] 2D**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 6)\n",
|
|
"out shape: (2, 6)\n",
|
|
"out: [0.5, 0.54, 0.6, 0.48, 0.7, 0.9, 0.494, 0.56, 0.5, 0.66, 0.44, 0.7]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 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",
|
|
"\n",
|
|
"result = activations.hard_sigmoid(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.hardSigmoid.2] 3D**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 2, 3)\n",
|
|
"out shape: (2, 2, 3)\n",
|
|
"out: [0.5, 0.54, 0.4, 0.48, 0.7, 0.9, 0.494, 0.96, 0.5, 0.66, 0.44, 0.7]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 2, 3)\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",
|
|
"\n",
|
|
"result = activations.hard_sigmoid(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"### linear"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**[activations.linear.0] 1D**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"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,)\n",
|
|
"out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"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",
|
|
"\n",
|
|
"result = activations.linear(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.linear.1] 2D**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"in: [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 6)\n",
|
|
"out shape: (2, 6)\n",
|
|
"out: [0.0, 0.2, 0.5, -0.1, 1.0, 2.0, -0.03, 0.3, 0.0, 0.8, -0.3, 1.0]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, 0.5, -0.1, 1, 2, -0.03, 0.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 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",
|
|
"\n",
|
|
"result = activations.linear(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": [
|
|
"**[activations.linear.2] 3D**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"in: [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"in shape: (2, 2, 3)\n",
|
|
"out shape: (2, 2, 3)\n",
|
|
"out: [0.0, 0.2, -0.5, -0.1, 1.0, 2.0, -0.03, 2.3, 0.0, 0.8, -0.3, 1.0]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data_in = [0, 0.2, -0.5, -0.1, 1, 2, -0.03, 2.3, 0, 0.8, -0.3, 1]\n",
|
|
"data_in_shape = (2, 2, 3)\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",
|
|
"\n",
|
|
"result = activations.linear(K.variable(np.array([arr_in])))\n",
|
|
"\n",
|
|
"arr_out = K.eval(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": {
|
|
"anaconda-cloud": {},
|
|
"kernelspec": {
|
|
"display_name": "Python [default]",
|
|
"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
|
|
}
|