hsleonis

FROM python:3.10-slim

# Install system dependencies for PIL, cloud SDKs, git, DVC, Airflow
RUN apt-get update && apt-get install -y \
    build-essential \
    git \
    libgl1-mesa-glx \
    libssl-dev \
    && rm -rf /var/lib/apt/lists/*

hsleonis

# Creating a Tuple
a_tuple = (1, 2, 3)

# Method 1: Tuple Concatenation
a_tuple = a_tuple + (4,)

print(a_tuple)
# Returns: (1, 2, 3, 4) <- an entirely new object

# Method 2: List Conversion

hsleonis

from multiprocessor import Pool
# multiprocessor.Pool API allows to distribute workload over several processes

# Function to apply a function over multiple cores
@print_timing
def parallel_apply(apply_func, groups, nb_cores):
    with Pool(nb_cores) as p:
        results = p.map(apply_func, groups)
    return pd.concat(results)

hsleonis

def encode(num: int):
    digits = str(num)
    out = 0
    prev = 0
    
    for tmp in digits:
        tmp = 10 if tmp=='0' else int(tmp)

        cur_prev = prev
        prev = tmp

hsleonis

from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

# image
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)

hsleonis

from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input
import numpy as np

# image
img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)

hsleonis

## Fine-tune InceptionV3 on a new set of classes

from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D

# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)

hsleonis

# -*- coding: utf-8 -*-
"""
AlexNet
"""

import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import Activation, Conv2D, AveragePooling2D, Dense, Flatten, BatchNormalization, MaxPool2D, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.datasets import mnist

hsleonis

# -*- coding: utf-8 -*-
"""
LeNet - Deep Learning Neural Network
"""

import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import Conv2D, AveragePooling2D, Dense, Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras.datasets import mnist

hsleonis

import tensorflow as tf
from tensorflow.python.platform import build_info as build
print(f"tensorflow version: {tf.__version__}")
print(f"Cuda Version: {build.build_info['cuda_version']}")
print(f"Cudnn version: {build.build_info['cudnn_version']}")