Diyago

#export PATH=~/anaconda3/bin:$PATH

from tqdm import tqdm 
import tensorflow as tf 
from keras.applications.resnet50 import ResNet50
from keras.layers import Flatten, Input
from keras.models import Model
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
import numpy as np

Diyago

from keras import layers


def residual_block(y, nb_channels, _strides=(1, 1), _project_shortcut=False):
    shortcut = y

    # down-sampling is performed with a stride of 2
    y = layers.Conv2D(nb_channels, kernel_size=(3, 3), strides=_strides, padding='same')(y)
    y = layers.BatchNormalization()(y)
    y = layers.LeakyReLU()(y)

Diyago

def reduce_mem_usage(data, verbose = True):
    start_mem = data.memory_usage().sum() / 1024**2
    if verbose:
        print('Memory usage of dataframe: {:.2f} MB'.format(start_mem))
    
    for col in data.columns:
        col_type = data[col].dtype
        
        if col_type != object:
            c_min = data[col].min()

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def fit_predict_model(dataframe, interval_width = 0.99, changepoint_range = 0.8):
    m = Prophet(daily_seasonality = False, yearly_seasonality = False, weekly_seasonality = False,
                seasonality_mode = 'multiplicative', 
                interval_width = interval_width,
                changepoint_range = changepoint_range)
    m = m.fit(dataframe)
    forecast = m.predict(dataframe)
    forecast['fact'] = dataframe['y'].reset_index(drop = True)
    return forecast
    

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def detect_anomalies(forecast):
    forecasted = forecast[['ds','trend', 'yhat', 'yhat_lower', 'yhat_upper', 'fact']].copy()
    #forecast['fact'] = df['y']

    forecasted['anomaly'] = 0
    forecasted.loc[forecasted['fact'] > forecasted['yhat_upper'], 'anomaly'] = 1
    forecasted.loc[forecasted['fact'] < forecasted['yhat_lower'], 'anomaly'] = -1

    #anomaly importances
    forecasted['importance'] = 0

Diyago

def plot_anomalies(forecasted):
    interval = alt.Chart(forecasted).mark_area(interpolate="basis", color = '#7FC97F').encode(
    x=alt.X('ds:T',  title ='date'),
    y='yhat_upper',
    y2='yhat_lower',
    tooltip=['ds', 'fact', 'yhat_lower', 'yhat_upper']
    ).interactive().properties(
        title='Anomaly Detection'
    )

Diyago

# based on https://github.com/BBarbosa/tflearn-image-recognition-toolkit/blob/
#                 4a0528dcfb206b1e45997f2fbc097aafacfa0fa0/scripts/html_link_parser.py

import re
import argparse

from PIL import Image
from io import BytesIO
from bs4 import BeautifulSoup
from skimage import io as skio

Diyago

conda install -c conda-forge keras
pip install git+https://github.com/qubvel/efficientnet
pip install git+https://github.com/qubvel/classification_models.git
pip install git+https://github.com/qubvel/segmentation_models
pip install git+https://github.com/albu/albumentations
pip install tta-wrapper

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def __init__(self, root_dir=r'../data/val_test', image_folder='img/', mask_folder='masks/', 
             batch_size=1, image_size=768, nb_y_features=1, 
             augmentation=None,
             suffle=True):
    self.image_filenames = listdir_fullpath(os.path.join(root_dir, image_folder))
    self.mask_names = listdir_fullpath(os.path.join(root_dir, mask_folder))
    self.batch_size = batch_size
    self.augmentation = augmentation
    self.image_size = image_size
    self.nb_y_features = nb_y_features

Diyago

def __getitem__(self, index):
      data_index_min = int(index*self.batch_size)
      data_index_max = int(min((index+1)*self.batch_size, len(self.image_filenames)))

      indexes = self.image_filenames[data_index_min:data_index_max]
      this_batch_size = len(indexes) # The last batch can be smaller than the others

      X = np.empty((this_batch_size, self.image_size, self.image_size, 3), dtype=np.float32)
      y = np.empty((this_batch_size, self.image_size, self.image_size, self.nb_y_features), dtype=np.uint8)