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

AmrutaKoshe

image=load_img("dog_photos/test/image4.jpg",target_size=(100,100))

image=img_to_array(image) 
image=image/255.0
prediction_image=np.array(image)
prediction_image= np.expand_dims(image, axis=0)

prediction=model.predict(prediction_image)
value=np.argmax(prediction)
move_name=mapper(value)

AmrutaKoshe

load_img("dog_photos/test/image4.jpg",target_size=(180,180))

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

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

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))