ArnoutDevos

How to upgrade a Heroku PostgreSQL database to a new plan

original: https://gist.github.com/simonw/50e14b9a3e829355d6d43f0f12f91e74

I started a project on a Hobby Dev plan (free, limit 10,000 rows), and then later needed to upgrade it to Hobby Basic ($9/month, limit 10,000,000 rows).

⚠️ In the following, replace <appname> with the name of your Heroku app.

What database(s) are connected?

ArnoutDevos

git clone https://github.com/rescience-c/template.git
cd template
brew tap caskroom/cask
brew cask install mactex

make draft

ArnoutDevos

rm -rf `find -type d -name .ipynb_checkpoints`

ArnoutDevos

# Inspiration from
# https://uoa-eresearch.github.io/eresearch-cookbook/recipe/2014/11/20/conda/
# and
# https://stackoverflow.com/questions/39604271/conda-environments-not-showing-up-in-jupyter-notebook

# Check if conda is up to date
conda update conda

# Create a virtual environment for your project
conda create -n myenv python=x.x anaconda

ArnoutDevos

import cv2

def imgread(path):
    print "Image:", path.split("/")[-1]
    # Read in the image using python opencv
    img = cv2.imread(path)
    img = img / 255.0
    print "Raw Image Shape: ", img.shape
    
    # Center crop the image

ArnoutDevos

@ops.RegisterGradient("GuidedRelu")
def _GuidedReluGrad(op, grad):
    return tf.where(0. < grad, gen_nn_ops._relu_grad(grad, op.outputs[0]), tf.zeros(grad.get_shape()))

ArnoutDevos

# Number of iterations to run.
ITERATIONS = 500

sess.run(tf.global_variables_initializer())
input_image = np.reshape(gen_image, ((1,) + gen_image.shape))
sess.run(model['input'].assign(input_image))
for it in range(ITERATIONS):
    sess.run(train_step)
    if it%50 == 0:
        # Print every 50 iteration.

ArnoutDevos

# Content loss emphasis.
ALPHA = 0.0025
# Style loss emphasis.
BETA = 1

total_loss = ALPHA * content_loss + BETA * style_loss

optimizer = tf.train.AdamOptimizer(2.0)
train_step = optimizer.minimize(total_loss)

ArnoutDevos

sess = tf.InteractiveSession()

model = load_vgg_model(VGG_MODEL)

# Construct content_loss using content_image.
content_image_list = np.reshape(content_image, ((1,) + content_image.shape))
sess.run(model['input'].assign(content_image_list))
content_loss = content_loss_func(sess, model)

# Construct style_loss using style_image.

ArnoutDevos

STYLE_LAYERS = [
    ('conv1_1', 0.5),
    ('conv2_1', 0.5),
    ('conv3_1', 0.5),
    ('conv4_1', 0.5),
    ('conv5_1', 0.5),
]

def style_loss_func(sess, model):
    def _gram_matrix(feat):