zenUnicorn

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zenUnicorn

# importing the required libarary
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.read_excel("online_retail_II.xlsx")

df.head()

#Bar chart visualisation

zenUnicorn

# install_certifi.py
#
# sample script to install or update a set of default Root Certificates
# for the ssl module.  Uses the certificates provided by the certifi package:
# https://pypi.python.org/pypi/certifi
import os
import os.path
import ssl
import stat
import subprocess

zenUnicorn

import comet_ml

experiment = comet_ml.Experiment(
    api_key="<Your API Key>",
    project_name="<Your Project Name>"
)

zenUnicorn

import comet_ml
api = comet_ml.api.API()

api.get()

experiment = api.get("zenunicorn/exp-notebooks/example 004")

zenUnicorn

import pandas as pd
import numpy as np
import warnings
import matplotlib.pyplot as plt

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import keras_tuner as kt

zenUnicorn

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import keras_tuner as kt

zenUnicorn

image_generator = ImageDataGenerator(rescale=1/255, validation_split=0.2)    

#Train & Validation Split 
train_dataset = image_generator.flow_from_directory(batch_size=32,
                                                 directory='data_cleaned/Train',
                                                 shuffle=True,
                                                 target_size=(224, 224), 
                                                 subset="training",
                                                 class_mode='categorical')

zenUnicorn

batch_1_img = train_dataset[0]
for i in range(0,32):
    img = batch_1_img[0][i]
    lab = batch_1_img[1][i]
    plt.imshow(img)
    plt.title(lab)
    plt.axis('off')
    plt.show()

zenUnicorn

model = keras.models.Sequential([
    keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape = [224, 224,3]),
    keras.layers.MaxPooling2D(),
    keras.layers.Conv2D(64, (2, 2), activation='relu'),
    keras.layers.MaxPooling2D(),
    keras.layers.Conv2D(64, (2, 2), activation='relu'),
    keras.layers.Flatten(),
    keras.layers.Dense(100, activation='relu'),
    keras.layers.Dense(2, activation ='softmax')
])