Replace reduce_sum with reduce_mean: adds an extra .1% accuracy :)
Aurélien Geron
parent
94914db82e
commit
87040e084e
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@ -77,10 +77,8 @@
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"cell_type": "code",
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"execution_count": 2,
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"collapsed": true
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import division, print_function, unicode_literals"
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@ -95,10 +93,8 @@
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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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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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@ -115,10 +111,8 @@
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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": true
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},
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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@ -141,10 +135,8 @@
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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": true
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"tf.reset_default_graph()"
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@ -159,10 +151,8 @@
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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": true
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},
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(42)\n",
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@ -185,7 +175,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -203,7 +193,7 @@
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -228,7 +218,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -286,10 +276,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = tf.placeholder(shape=[None, 28, 28, 1], dtype=tf.float32, name=\"X\")"
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@ -311,10 +299,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"caps1_n_maps = 32\n",
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@ -331,10 +317,8 @@
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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": true
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},
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"conv1_params = {\n",
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@ -356,10 +340,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"conv1 = tf.layers.conv2d(X, name=\"conv1\", **conv1_params)\n",
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@ -382,10 +364,8 @@
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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": true
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},
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"caps1_raw = tf.reshape(conv2, [-1, caps1_n_caps, caps1_n_dims],\n",
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@ -407,10 +387,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"def squash(s, axis=-1, epsilon=1e-7, name=None):\n",
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@ -432,10 +410,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"caps1_output = squash(caps1_raw, name=\"caps1_output\")"
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@ -478,10 +454,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"caps2_n_caps = 10\n",
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@ -568,10 +542,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"init_sigma = 0.01\n",
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@ -591,10 +563,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"batch_size = tf.shape(X)[0]\n",
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@ -610,10 +580,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"caps1_output_expanded = tf.expand_dims(caps1_output, -1,\n",
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@ -633,7 +601,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -649,7 +617,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -665,10 +633,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"caps2_predicted = tf.matmul(W_tiled, caps1_output_tiled,\n",
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@ -684,7 +650,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -714,10 +680,8 @@
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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": true
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},
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"raw_weights = tf.zeros([batch_size, caps1_n_caps, caps2_n_caps, 1, 1],\n",
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@ -747,10 +711,8 @@
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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": true
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},
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"routing_weights = tf.nn.softmax(raw_weights, dim=2, name=\"routing_weights\")"
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@ -765,10 +727,8 @@
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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": true
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},
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"weighted_predictions = tf.multiply(routing_weights, caps2_predicted,\n",
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@ -797,10 +757,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"caps2_output_round_1 = squash(weighted_sum, axis=-2,\n",
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@ -809,7 +767,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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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": 30,
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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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": 31,
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -885,10 +843,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"caps2_output_round_1_tiled = tf.tile(\n",
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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"agreement = tf.matmul(caps2_predicted, caps2_output_round_1_tiled,\n",
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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"raw_weights_round_2 = tf.add(raw_weights, agreement,\n",
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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"routing_weights_round_2 = tf.nn.softmax(raw_weights_round_2,\n",
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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"caps2_output = caps2_output_round_2"
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"outputs": [],
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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": 38,
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"metadata": {},
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"outputs": [],
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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": 39,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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"def safe_norm(s, axis=-1, epsilon=1e-7, keep_dims=False, name=None):\n",
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 41,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_proba = safe_norm(caps2_output, axis=-2, name=\"y_proba\")"
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 42,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_proba_argmax = tf.argmax(y_proba, axis=2, name=\"y_proba\")"
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred = tf.squeeze(y_proba_argmax, axis=[1,2], name=\"y_pred\")"
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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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": 45,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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"y = tf.placeholder(shape=[None], dtype=tf.int64, name=\"y\")"
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 47,
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"metadata": {},
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"outputs": [],
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"source": [
|
||||
"m_plus = 0.9\n",
|
||||
|
|
@ -1232,10 +1168,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"T = tf.one_hot(y, depth=caps2_n_caps, name=\"T\")"
|
||||
|
|
@ -1250,7 +1184,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1267,7 +1201,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1283,10 +1217,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"caps2_output_norm = safe_norm(caps2_output, axis=-2, keep_dims=True,\n",
|
||||
|
|
@ -1302,10 +1234,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 52,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"present_error_raw = tf.square(tf.maximum(0., m_plus - caps2_output_norm),\n",
|
||||
|
|
@ -1323,10 +1253,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"absent_error_raw = tf.square(tf.maximum(0., caps2_output_norm - m_minus),\n",
|
||||
|
|
@ -1344,10 +1272,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"L = tf.add(T * present_error, lambda_ * (1.0 - T) * absent_error,\n",
|
||||
|
|
@ -1363,10 +1289,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"margin_loss = tf.reduce_mean(tf.reduce_sum(L, axis=1), name=\"margin_loss\")"
|
||||
|
|
@ -1409,10 +1333,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mask_with_labels = tf.placeholder_with_default(False, shape=(),\n",
|
||||
|
|
@ -1428,10 +1350,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"reconstruction_targets = tf.cond(mask_with_labels, # condition\n",
|
||||
|
|
@ -1458,10 +1378,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 58,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"reconstruction_mask = tf.one_hot(reconstruction_targets,\n",
|
||||
|
|
@ -1478,7 +1396,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"execution_count": 59,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1494,7 +1412,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1510,10 +1428,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"reconstruction_mask_reshaped = tf.reshape(\n",
|
||||
|
|
@ -1530,10 +1446,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"caps2_output_masked = tf.multiply(\n",
|
||||
|
|
@ -1543,7 +1457,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1559,10 +1473,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"decoder_input = tf.reshape(caps2_output_masked,\n",
|
||||
|
|
@ -1579,7 +1491,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1602,10 +1514,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 66,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"n_hidden1 = 512\n",
|
||||
|
|
@ -1615,10 +1525,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with tf.name_scope(\"decoder\"):\n",
|
||||
|
|
@ -1649,16 +1557,14 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 68,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_flat = tf.reshape(X, [-1, n_output], name=\"X_flat\")\n",
|
||||
"squared_difference = tf.square(X_flat - decoder_output,\n",
|
||||
" name=\"squared_difference\")\n",
|
||||
"reconstruction_loss = tf.reduce_sum(squared_difference,\n",
|
||||
"reconstruction_loss = tf.reduce_mean(squared_difference,\n",
|
||||
" name=\"reconstruction_loss\")"
|
||||
]
|
||||
},
|
||||
|
|
@ -1678,10 +1584,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 68,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 69,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"alpha = 0.0005\n",
|
||||
|
|
@ -1712,10 +1616,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 70,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"correct = tf.equal(y, y_pred, name=\"correct\")\n",
|
||||
|
|
@ -1738,10 +1640,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 71,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"optimizer = tf.train.AdamOptimizer()\n",
|
||||
|
|
@ -1764,10 +1664,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 71,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 72,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"init = tf.global_variables_initializer()\n",
|
||||
|
|
@ -1804,7 +1702,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 72,
|
||||
"execution_count": 73,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1870,7 +1768,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Training is finished, we reached over 99.3% accuracy on the validation set after just 5 epochs, things are looking good. Now let's evaluate the model on the test set."
|
||||
"Training is finished, we reached over 99.4% accuracy on the validation set after just 5 epochs, things are looking good. Now let's evaluate the model on the test set."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -1882,7 +1780,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 73,
|
||||
"execution_count": 74,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1915,7 +1813,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We reach 99.43% accuracy on the test set. Pretty nice. :)"
|
||||
"We reach 99.53% accuracy on the test set. Pretty nice. :)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -1934,7 +1832,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 74,
|
||||
"execution_count": 75,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1966,7 +1864,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 75,
|
||||
"execution_count": 76,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2022,7 +1920,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 76,
|
||||
"execution_count": 77,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2038,10 +1936,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 77,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 78,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def tweak_pose_parameters(output_vectors, min=-0.5, max=0.5, n_steps=11):\n",
|
||||
|
|
@ -2062,10 +1958,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 78,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 79,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"n_steps = 11\n",
|
||||
|
|
@ -2084,7 +1978,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 79,
|
||||
"execution_count": 80,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2108,10 +2002,8 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 80,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 81,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tweak_reconstructions = decoder_output_value.reshape(\n",
|
||||
|
|
@ -2127,7 +2019,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 81,
|
||||
"execution_count": 82,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2161,9 +2053,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue