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View wp_2017_full_width.css
@media screen and (min-width: 48em) {
#content > .wrap {
max-width: 100%
#content #primary {
width: 65%;
View att512.yml
batch_size: 128
buckets: 10,20,30,40
dev_source: newstest2013.tok.bpe.32000.en
attention.dim: 512
attention.score_type: bahdanau
class: GRUCell
num_units: 512
# Install build tools
sudo apt-get update
sudo apt-get install -y build-essential git python-pip libfreetype6-dev libxft-dev libncurses-dev libopenblas-dev gfortran python3-matplotlib libblas-dev liblapack-dev libatlas-base-dev python3-dev python3-pydot linux-headers-generic linux-image-extra-virtual unzip python3-numpy swig python3-pandas python-sklearn unzip python3-pip python3-venv
# Install CUDA 7
# wget
sudo dpkg -i cuda-repo-ubuntu1504_7.5-18_amd64.deb && rm cuda-repo-ubuntu1504_7.5-18_amd64.deb
sudo apt-get update
sudo apt-get install -y cuda
View gist:9df7dd2553b0aa8db808
Variable_2: /job:localhost/replica:0/task:0/cpu:0
I tensorflow/core/common_runtime/] Variable_2: /job:localhost/replica:0/task:0/cpu:0
zeros: /job:localhost/replica:0/task:0/cpu:0
I tensorflow/core/common_runtime/] zeros: /job:localhost/replica:0/task:0/cpu:0
Variable_2/Assign: /job:localhost/replica:0/task:0/cpu:0
I tensorflow/core/common_runtime/] Variable_2/Assign: /job:localhost/replica:0/task:0/cpu:0
Variable_1: /job:localhost/replica:0/task:0/cpu:0
I tensorflow/core/common_runtime/] Variable_1: /job:localhost/replica:0/task:0/cpu:0
truncated_normal/stddev: /job:localhost/replica:0/task:0/cpu:0
I tensorflow/core/common_runtime/] truncated_normal/stddev: /job:localhost/replica:0/task:0/cpu:0
# Helper function to plot a decision boundary.
# If you don't fully understand this function don't worry, it just generates the contour plot below.
def plot_decision_boundary(pred_func):
# Set min and max values and give it some padding
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
h = 0.01
# Generate a grid of points with distance h between them
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Predict the function value for the whole gid