Dapid

import numpy as np
import keras_cv


model = keras_cv.models.RetinaNet(backbone=keras_cv.models.MobileNetV3Backbone.from_preset('mobilenet_v3_small_imagenet'),
                                              num_classes=2,
                                              bounding_box_format="xywh",
                                              )
image= np.random.randint(0, 255, size=(1, 640, 480, 3), dtype=np.uint8)
model(image)

Dapid

import numpy as np
import scipy.optimize as spo
import scipy.stats as sps
import matplotlib.pyplot as plt

x, y = np.array([[3.16275414, 3.79136358],
                 [3.06332232, 3.56686702],
                 [2.71045949, 3.65764056],
                 [3.31620986, 3.9009491 ],
                 [3.0538026 , 3.77374607],

Dapid

from __future__ import division
import collections

from keras import backend as K
from keras import activations, initializations, regularizers, constraints
from keras.engine import InputSpec
from keras.engine.topology import Layer


class Tensordot(Layer):

Dapid

import pandas as pd
import pylab as plt

df_allcauses = pd.read_excel('datasetfinalcorrected3.xlsx', sheet_name='Table 2', header=4, nrows=38)
df_covid = pd.read_excel('datasetfinalcorrected3.xlsx', sheet_name='Table 1', header=4, nrows=38)


for k in df_covid.keys():
    if k.startswith('Age-st'):
        df_covid[k] = pd.to_numeric(df_covid[k], errors='coerce').fillna(0)

Dapid

import numpy as np
import matplotlib.pyplot as plt

import cartopy.crs as ccrs
import cartopy.feature as cfeature


lon = 10 + 15 * np.random.random(30)
lat = 55 + 15 * np.random.random(30)
data = np.random.randn(30)

Dapid

# Run this to get the example file

import tables
import numpy as np

h5 = tables.open_file('onefile.h5', 'w', filters=tables.Filters(8, 'lzo'))
g = h5.create_group(h5.root, 'data')

h5.create_carray(g, 'data_array', obj=np.random.random((int(1e6), 20)))
h5.close()

Dapid

[Version]
AppVersion=5.7
Version=346

[General]
Rank=-1
ColorLabel=0
InTrash=false

[Exposure]

Dapid

\version "2.19.83"
\language "espanol"

\header {
  title = "Amazing Grace"
  composer = "Trad. Scottish"
  %tagline = ""
}

Dapid

import numpy as np
import netCDF4 as nc
from scipy import stats

import pylab as plt
import seaborn as sns

rootgrp = nc.Dataset('HadCRUT.4.6.0.0.median.nc')  

t = rootgrp['time'][:] / 365 + 1850

Dapid

import os

# os.environ['CUDA_VISIBLE_DEVICES'] = '1'

import time

import numpy

import jax.numpy as np
from jax import random, grad, jit