felipessalvatore

import matplotlib.pyplot as plt
from datetime import date

#data


logic_tuple =  [(1936, 3), (1937, 6), (1938, 3), (1939, 5),
  (1940, 2), (1941, 2), (1942, 7), (1943, 1),
  (1944, 2), (1945, 2), (1947, 2), (1948, 4),
  (1949, 7), (1950, 3), (1951, 2), (1952, 5),

felipessalvatore

from sklearn import linear_model
import numpy as np
import matplotlib.pyplot as plt

# generating fake linear data. 
# Where x = square meter, y = house price

cond = False

while not cond:

felipessalvatore

Setting Jupyter in a remote computer

In REMOTE

i) create config file

$ jupyter notebook --generate-config

ii) create password

felipessalvatore

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc

sns.set_style("darkgrid")

# for using latex in plt it requires one installation:
# $ sudo apt install dvipng
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})

felipessalvatore

# strided_slice(
#     input_,
#     begin,
#     end,
#     strides=None,
#     begin_mask=0,
#     end_mask=0,
#     ellipsis_mask=0,
#     new_axis_mask=0,
#     shrink_axis_mask=0,

felipessalvatore

# range_input_producer(
#                      limit,
#                      num_epochs=None,
#                      shuffle=True,
#                      seed=None,
#                      capacity=32,
#                      shared_name=None,
#                      name=None)

# Produces the integers from 0 to limit-1 in a queue.

felipessalvatore

import numpy as np
import tensorflow as tf

NUM_THREADS = 4

data = np.array([[0.2, 0.3], [0.4, 0.9], [0.8, -1.2], [-1.3, -0.2]])
target = np.array([1, 0, 1, 0])

queue = tf.FIFOQueue(capacity=3,
                     dtypes=[tf.float32, tf.int32],

felipessalvatore

#code adapted from http://tillbergmann.com/blog/python-gradient-descent.html

%matplotlib inline
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from scipy import stats 

from sklearn.datasets.samples_generator import make_regression 

felipessalvatore

import numpy as np
import random
import unittest
from math import isnan


def softmax(x):
    """
    Compute the softmax function for each row of the input x.
    It is crucial that this function is optimized for speed because

felipessalvatore

import tracemalloc

tracemalloc.start()

# ... run your application ...

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics('lineno')

print("[ Top 10 ]")