sachinkale

import json
import subprocess
import os

# Base directory where Terraform configurations will be saved
base_output_dir = 'terraform_roles_policies'
#policies_dir = os.path.join(base_output_dir, 'policies')

# Create directories if they don't exist
#os.makedirs(policies_dir, exist_ok=True)

sachinkale

from tensorflow.keras.models import load_model
import tensorflow as tf
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
import numpy as np
import json


model = load_model('vggK5-weights-best.h5')

sachinkale

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

from PIL import Image
import re 
import json

sachinkale

$ python3 vgg.py
Found 758 images belonging to 19 classes.
Found 180 images belonging to 19 classes.
2020-09-17 14:42:07.878829: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-09-17 14:42:07.901823: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fbe0c406470 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-09-17 14:42:07.901927: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================

sachinkale

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss=history.history['loss']
val_loss=history.history['val_loss']

epochs_range = range(epochs)

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)

sachinkale

model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])


model.summary()
history = model.fit(
    x = training_set,
    epochs=epochs,
    validation_data=test_set
    
)

sachinkale

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation=tf.keras.layers.LeakyReLU(alpha=0.3), input_shape=(IMG_HEIGHT,IMG_WIDTH , 3)))
model.add(Conv2D(32, (3, 3), activation=tf.keras.layers.LeakyReLU(alpha=0.3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, kernel_size=(3, 3), activation=tf.keras.layers.LeakyReLU(alpha=0.3)))
model.add(Conv2D(64, (3, 3), activation=tf.keras.layers.LeakyReLU(alpha=0.3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

sachinkale

from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
import os
import numpy as np
import matplotlib.pyplot as plt

batch_size = 24
epochs = 200
IMG_HEIGHT = 150

sachinkale

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
model = ResNet50(weights='imagenet')
img_path = 'boris-smokrovic-Ori_JWlqVpc-unsplash.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

sachinkale

# import the necessary packages
from os import walk
import numpy as np
import argparse
import time
import cv2
import os
import re
# construct the argument parse and parse the arguments