mikkokotila

import datetime, coronacaster, pandas

data = coronacaster.get_data_from_eu()

out = []
countries = data.countries
for country in countries:
  
  # parameters start
  for ftype in ['poly1', 'poly2', 'poly3', 'logis', 'sigmoid', 'scurve']:

mikkokotila

function csvImport(path_to_file) {

d3.csv(path_to_file).then(prepData);

  function prepData(dataset) {

      // create the columns

      var keys = Object.keys(dataset[0])
      var headers = []

mikkokotila

# name of the service
NAME=$1

# exec and service file paths
EXEC_FILE=$2
SERVICE_FILE=/etc/systemd/system/"$NAME".service

# timing
TIMER_FILE=/etc/systemd/system/"$NAME".timer
TIMING=$3

mikkokotila

import talos as ta

x, y = ta.templates.datasets.cervical_cancer()
p = ta.templates.params.cervical_cancer()
model = ta.templates.models.cervical_cancer()

mikkokotila

import signs as signs
import pandas as pd

# load some text
df = pd.read_csv('tweets.csv').text

# load vectors
e = signs.Embeds("glove.twitter.27B.25d.txt")

# get Keras embeddings layer

mikkokotila

import talos as ta
from keras.models import Sequential
from keras.layers import Dense

def minimal():

     x, y = ta.datasets.iris()

     p = {'activation':['relu', 'elu'],
          'optimizer': ['Nadam', 'Adam'],

mikkokotila

import talos
from keras.models import Sequential
from keras.layers import Dense

def minimal():

    x, y = talos.templates.datasets.iris()

    p = {'activation':['relu', 'elu'],
         'optimizer': ['Nadam', 'Adam'],

mikkokotila

def iris_model(x_train, y_train, x_val, y_val):

    model = Sequential()
    model.add(Dense(32, input_dim=8, activation='adam'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(optimizer='relu', loss='binary_crossentropy')

    out = model.fit(x_train, y_train,
                    batch_size=24,
                    epochs=100,

mikkokotila

p = {'lr': (0.8, 1.2, 3),
     'first_neuron':[4, 8, 16, 32, 64],
     'hidden_layers':[0, 1, 2],
     'batch_size': (1, 5, 5),
     'epochs': [50, 100, 150],
     'dropout': (0, 0.2, 3),
     'weight_regulizer':[None],
     'emb_output_dims': [None],
     'shape':['brick','long_funnel'],
     'kernel_initializer': ['uniform','normal'],

mikkokotila

# and run the experiment
t = ta.Scan(x=x,
            y=y,
            model=breast_cancer_model,
            grid_downsample=0.01, 
            params=p,
            dataset_name='breast_cancer',
            experiment_no='1')