| | |
| | """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() |
