Upload model.py

model.py

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+# -*- 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()