AmrutaKoshe

model.compile(optimizer='adam',
             loss='categorical_crossentropy',
             metrics=['accuracy'])
             
history = model.fit(train_generator, validation_data=valid_generator,
                   steps_per_epoch=train_generator.n//train_generator.batch_size,
                   validation_steps=valid_generator.n//valid_generator.batch_size,
                   epochs=120)

AmrutaKoshe

model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=(100,100,3), activation='relu', padding = 'same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding = 'same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding = 'same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

AmrutaKoshe

score = model.evaluate(val_gen, steps= len(val_gen))

for idx, metric in enumerate(model.metrics_names):
    print('{}:{}'.format(metric, score[idx]))

AmrutaKoshe

model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=(100,100,3), activation='relu', padding = 'same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding = 'same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding = 'same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

AmrutaKoshe

train_datagen = ImageDataGenerator(vertical_flip=True,
                                horizontal_flip=True,
                                rotation_range=40,
                                width_shift_range=0.2,
                                height_shift_range=0.2,
                                zoom_range=0.1,
                                validation_split=0.2)

val_datagen = ImageDataGenerator()

AmrutaKoshe

from sklearn.model_selection import train_test_split
X_train, X_val, y_train, y_val = train_test_split(Train_Imgs, Train_Lbls, shuffle = True, test_size = 0.2, random_state = 42)
print('Shape of X_train: {}, y_train: {} '.format(X_train.shape, y_train.shape))
print('Shape of X_val: {}, y_val: {} '.format(X_val.shape, y_val.shape))

AmrutaKoshe

def read_images():
    Images, Labels = [], []
    for root, dirs, files in os.walk('gems/train/'):
        f = os.path.basename(root) # get class name - Amethyst, Onyx, etc       
        for file in files:
            Labels.append(f)
            try:
                image = cv2.imread(root+'/'+file)              # read the image (OpenCV)
                image = cv2.resize(image,(int(img_w), int(img_h)))       # resize the image (images are different sizes)
                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # converts an image from BGR color space to RGB

AmrutaKoshe

Name=[]
for file in os.listdir(directory):
    Name+=[file]
    
gems_map = dict(zip(Name, [t for t in range(len(Name))]))
print(gems_map)
r_gems_map=dict(zip([t for t in range(len(Name))],Name)) 

AmrutaKoshe

wget -N "https://cainvas-static.s3.amazonaws.com/media/user_data/AmrutaKoshe/gems.zip"
!unzip -qo gems.zip

AmrutaKoshe

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import wget
import os
import cv2