Merge branch 'master' of github.com:ageron/handson-ml

11_deep_learning.ipynb

@@ -1197,6 +1197,7 @@
     "n_hidden2 = 50\n",
     "n_hidden3 = 50\n",
     "n_hidden4 = 50\n",
+    "n_hidden5 = 50\n",
     "n_outputs = 10\n",
     "\n",
     "X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\"X\")\n",
@@ -4648,7 +4649,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.5.5"
   },
   "nav_menu": {
    "height": "360px",

README.md

@@ -51,7 +51,7 @@ If you choose to use Anaconda, read the next section, or else jump to the [Using
 When using Anaconda, you can optionally create an isolated Python environment dedicated to this project. This is recommended as it makes it possible to have a different environment for each project (e.g. one for this project), with potentially different libraries and library versions:
 
     $ conda create -n mlbook python=3.5 anaconda
-    $ source activate mlbook
+    $ conda activate mlbook
 
 This creates a fresh Python 3.5 environment called `mlbook` (you can change the name if you want to), and it activates it. This environment contains all the scientific libraries that come with Anaconda. This includes all the libraries we will need (NumPy, Matplotlib, Pandas, Jupyter and a few others), except for TensorFlow, so let's install it:
 

docker/Dockerfile

@@ -1,4 +1,4 @@
-FROM continuumio/anaconda3
+FROM continuumio/anaconda3:5.2.0
 
 RUN apt-get update && apt-get upgrade -y \
     && apt-get install -y \

docker/docker-compose.yml

@@ -9,6 +9,7 @@ services:
         - userid=1000
     container_name: handson-ml
     image: handson-ml
+    restart: unless-stopped    
     logging:
       driver: json-file
       options:
@@ -18,4 +19,4 @@ services:
       - "6006:6006"
     volumes:
       - ../:/home/devel/handson-ml
-    command: /opt/conda/bin/jupyter notebook --ip='*' --port=8888 --no-browser
+    command: /opt/conda/bin/jupyter notebook --ip='0.0.0.0' --port=8888 --no-browser