File size: 2,470 Bytes
f397fe2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | # -*- 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() |