# -*- coding: utf-8 -*-
"""Copy of convolutional_neural_network.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1jcU6XSixUKLWzQxMdwEdP6MUoac4HvAq

# Convolutional Neural Network

### Importing the libraries
"""

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import load_model

"""## Part 1 - Data Preprocessing

### Preprocessing the Training set
"""

train_datagen=ImageDataGenerator(rescale=1./255,shear_range=0.2,zoom_range=0.2,horizontal_flip=True)
training_set=train_datagen.flow_from_directory(r'dataset\training_set',target_size=(64,64),batch_size=32,class_mode='binary')

"""### Preprocessing the Test set"""

test_datagen=ImageDataGenerator(rescale=1./255,shear_range=0.2,zoom_range=0.2,horizontal_flip=True)
test_set=test_datagen.flow_from_directory(r'dataset\test_set',target_size=(64,64),batch_size=32,class_mode='binary')

"""## Part 2 - Building the CNN

### Initialising the CNN
"""

cnn=tf.keras.models.Sequential()

"""### Step 1 - Convolution"""

cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu',input_shape=[64,64,3]))

"""### Step 2 - Pooling"""

cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))

"""### Adding a second convolutional layer"""

cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))

"""### Step 3 - Flattening"""

cnn.add(tf.keras.layers.Flatten())

"""### Step 4 - Full Connection"""

cnn.add(tf.keras.layers.Dense(units=128,activation='relu'))

"""### Step 5 - Output Layer"""

cnn.add(tf.keras.layers.Dense(units=1,activation='sigmoid'))

"""## Part 3 - Training the CNN

### Compiling the CNN
"""

cnn.compile(optimizer='adam', loss='binary_crossentropy',metrics=['accuracy'])

"""### Training the CNN on the Training set and evaluating it on the Test set"""

cnn.fit(x=training_set,validation_data=test_set,epochs=25)

"""## Part 4 - Making a single prediction"""
cnn.save('cnn_model.h5')
import numpy as np
from keras.preprocessing import image
test_image=image.load_img(r'dataset\single_prediction\cat_or_dog_1.jpg',target_size=(64,64))
test_image=image.img_to_array(test_image)
test_image=np.expand_dims(test_image,axis=0)
res=cnn.predict(test_image)
training_set.class_indices
if res[0][0]==1:
  prediction='dog'
else:
  prediction='cat'

print(prediction)
cnn.summary()