├── requirements_keras.txt ├── requirements_tensorflow.txt ├── Using Keras ├── data │ ├── alien_test │ │ └── README.md │ ├── train │ │ └── README.md │ └── test │ │ └── README.md ├── Testing.py └── Training.py ├── Using Tensorflow ├── data │ ├── test │ │ └── README.md │ └── train │ │ └── README.md ├── README.md ├── dataset.py ├── Prediction.py └── Training.py ├── README.md └── LICENSE /requirements_keras.txt: -------------------------------------------------------------------------------- 1 | sys 2 | os 3 | time 4 | keras==2.2.0 5 | numpy==1.22.0 6 | -------------------------------------------------------------------------------- /requirements_tensorflow.txt: -------------------------------------------------------------------------------- 1 | os 2 | time 3 | cv2 4 | glob 5 | dataset==1.1.0 6 | tensorflow==2.11.1 7 | numpy==1.22.0 8 | sklearn==0.19.1 9 | -------------------------------------------------------------------------------- /Using Keras/data/alien_test/README.md: -------------------------------------------------------------------------------- 1 | Upload all the images to be tested here. Make sure they are not the images you have trained your system with. 2 | -------------------------------------------------------------------------------- /Using Tensorflow/data/test/README.md: -------------------------------------------------------------------------------- 1 | Upload all the images to be tested here. Make sure they are not the images you have trained your system with. 2 | -------------------------------------------------------------------------------- /Using Tensorflow/README.md: -------------------------------------------------------------------------------- 1 | While execution of Training.py, if there is an AssertionError, change the variable batch_size to a different value that is a factor of the total training images. To change the batch_size in Training.py, go to line number 14 and change the value. -------------------------------------------------------------------------------- /Using Tensorflow/data/train/README.md: -------------------------------------------------------------------------------- 1 | In this data/train folder, 2 | 3 | Create sub-folders of all the classes of images. For example, if you want to train your system with 3 classes - Chair, Motorcycle and Soccer Ball, create the following sub-folders: 4 | ``` 5 | data 6 | | 7 | |-train 8 | | 9 | |-Chair 10 | | 11 | |-ChairImag001.jpg 12 | |-ChairImg002.jpg 13 | |..... 14 | |-Motocrycle 15 | | 16 | |-Motorcycle001.jpg 17 | |-Motorcycle002.jpg 18 | |..... 19 | |-Soccer Ball 20 | |-Soccer_Ball-001.jpg 21 | |-Soccer_Ball-002.jpg 22 | ``` 23 | If you have any more classes, create the sub-folders in the above similar fashion. 24 | -------------------------------------------------------------------------------- /Using Keras/data/train/README.md: -------------------------------------------------------------------------------- 1 | In this data/train folder, 2 | 3 | Create sub-folders of all the classes of images. For example, if you want to train your system with 3 classes - Chair, Motorcycle and Soccer Ball, create the following sub-folders: 4 | 5 | ``` 6 | data 7 | | 8 | |-train 9 | | 10 | |-Chair 11 | | 12 | |-ChairImag001.jpg 13 | |-ChairImg002.jpg 14 | |..... 15 | |-Motocrycle 16 | | 17 | |-Motorcycle001.jpg 18 | |-Motorcycle002.jpg 19 | |..... 20 | |-Soccer Ball 21 | |-Soccer_Ball-001.jpg 22 | |-Soccer_Ball-002.jpg 23 | 24 | ``` 25 | If you have any more classes, create the sub-folders in the above similar fashion. 26 | -------------------------------------------------------------------------------- /Using Keras/data/test/README.md: -------------------------------------------------------------------------------- 1 | NOTE: This folder is for validation purposes. The system checks its own training models by validating the model using these inputs. 2 | 3 | In this data/test folder, 4 | 5 | Create sub-folders of all the classes of images. For example, if you want to train your system with 3 classes - Chair, Motorcycle and Soccer Ball, create the following sub-folders: 6 | 7 | ``` 8 | data 9 | | 10 | |-test 11 | | 12 | |-Chair 13 | | 14 | |-ChairImag001.jpg 15 | |-ChairImg002.jpg 16 | |..... 17 | |-Motocrycle 18 | | 19 | |-Motorcycle001.jpg 20 | |-Motorcycle002.jpg 21 | |..... 22 | |-Soccer Ball 23 | |-Soccer_Ball-001.jpg 24 | |-Soccer_Ball-002.jpg 25 | ``` 26 | 27 | If you have any more classes, create the sub-folders in the above similar fashion. 28 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Image-Classification-by-Keras-and-Tensorflow 2 | Image Classification using Keras as well as Tensorflow. Image Classification is a Machine Learning module that trains itself from an existing dataset of multiclass images and develops a model for future prediction of similar images not encountered during training. Developed using Convolutional Neural Network (CNN). 3 | 4 | This repository contains implementation for multiclass image classification using Keras as well as Tensorflow. These two codes have no interdependecy on each other. 5 | 6 | ## Convolutional Neural Network (CNN) 7 | To be updated soon. 8 | 9 | ## Modules Required 10 | You can download the modules in the respective requirements.txt for each implementation. 11 | 12 | ### Implementation using Keras 13 | sys
14 | os
15 | time
16 | keras==2.2.0
17 | numpy==1.14.5
18 | 19 | ### Implementation using Tensorflow 20 | os
21 | time
22 | cv2
23 | glob
24 | dataset==1.1.0
25 | tensorflow==1.15.0
26 | numpy==1.14.5
27 | sklearn==0.19.1
28 | 29 | ## Steps 30 | 1. Install the modules required based on the type of implementation. 31 | 2. Download the dataset you want to train and predict your system with. For sample data, you can download the [101_ObjectCategories (131Mbytes)]. 32 | 3. Construct the folder sub-structure required. 33 | ### Keras 34 | 35 | [![Keras.png](https://s33.postimg.cc/llzbwcfsf/Keras.png)](https://postimg.cc/image/i2de6jd2j/) 36 | 37 | ### Tensorflow 38 | 39 | [![Tensorflow.png](https://s33.postimg.cc/kjp5dwzkv/Tensorflow.png)](https://postimg.cc/image/ame4kuryz/) 40 | 41 | [101_ObjectCategories (131Mbytes)]: http://www.vision.caltech.edu/Image_Datasets/Caltech101/ 42 | -------------------------------------------------------------------------------- /Using Keras/Testing.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array 4 | from keras.models import Sequential, load_model 5 | import time 6 | 7 | start = time.time() 8 | 9 | #Define Path 10 | model_path = './models/model.h5' 11 | model_weights_path = './models/weights.h5' 12 | test_path = 'data/alien_test' 13 | 14 | #Load the pre-trained models 15 | model = load_model(model_path) 16 | model.load_weights(model_weights_path) 17 | 18 | #Define image parameters 19 | img_width, img_height = 150, 150 20 | 21 | #Prediction Function 22 | def predict(file): 23 | x = load_img(file, target_size=(img_width,img_height)) 24 | x = img_to_array(x) 25 | x = np.expand_dims(x, axis=0) 26 | array = model.predict(x) 27 | result = array[0] 28 | #print(result) 29 | answer = np.argmax(result) 30 | if answer == 1: 31 | print("Predicted: chair") 32 | elif answer == 0: 33 | print("Predicted: Motorbikes") 34 | elif answer == 2: 35 | print("Predicted: soccer_ball") 36 | 37 | return answer 38 | 39 | #Walk the directory for every image 40 | for i, ret in enumerate(os.walk(test_path)): 41 | for i, filename in enumerate(ret[2]): 42 | if filename.startswith("."): 43 | continue 44 | 45 | print(ret[0] + '/' + filename) 46 | result = predict(ret[0] + '/' + filename) 47 | print(" ") 48 | 49 | #Calculate execution time 50 | end = time.time() 51 | dur = end-start 52 | 53 | if dur<60: 54 | print("Execution Time:",dur,"seconds") 55 | elif dur>60 and dur<3600: 56 | dur=dur/60 57 | print("Execution Time:",dur,"minutes") 58 | else: 59 | dur=dur/(60*60) 60 | print("Execution Time:",dur,"hours") 61 | -------------------------------------------------------------------------------- /Using Keras/Training.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | from keras.preprocessing.image import ImageDataGenerator 4 | from keras import optimizers 5 | from keras.models import Sequential 6 | from keras.layers import Dropout, Flatten, Dense, Activation 7 | from keras.layers.convolutional import Convolution2D, MaxPooling2D 8 | from keras import callbacks 9 | import time 10 | 11 | start = time.time() 12 | 13 | DEV = False 14 | argvs = sys.argv 15 | argc = len(argvs) 16 | 17 | if argc > 1 and (argvs[1] == "--development" or argvs[1] == "-d"): 18 | DEV = True 19 | 20 | if DEV: 21 | epochs = 2 22 | else: 23 | epochs = 20 24 | 25 | train_data_path = 'data/train' 26 | validation_data_path = 'data/test' 27 | 28 | """ 29 | Parameters 30 | """ 31 | img_width, img_height = 150, 150 32 | batch_size = 32 33 | samples_per_epoch = 1000 34 | validation_steps = 300 35 | nb_filters1 = 32 36 | nb_filters2 = 64 37 | conv1_size = 3 38 | conv2_size = 2 39 | pool_size = 2 40 | classes_num = 3 41 | lr = 0.0004 42 | 43 | model = Sequential() 44 | model.add(Convolution2D(nb_filters1, conv1_size, conv1_size, border_mode ="same", input_shape=(img_width, img_height, 3))) 45 | model.add(Activation("relu")) 46 | model.add(MaxPooling2D(pool_size=(pool_size, pool_size))) 47 | 48 | model.add(Convolution2D(nb_filters2, conv2_size, conv2_size, border_mode ="same")) 49 | model.add(Activation("relu")) 50 | model.add(MaxPooling2D(pool_size=(pool_size, pool_size), dim_ordering='th')) 51 | 52 | model.add(Flatten()) 53 | model.add(Dense(256)) 54 | model.add(Activation("relu")) 55 | model.add(Dropout(0.5)) 56 | model.add(Dense(classes_num, activation='softmax')) 57 | 58 | model.compile(loss='categorical_crossentropy', 59 | optimizer=optimizers.RMSprop(lr=lr), 60 | metrics=['accuracy']) 61 | 62 | train_datagen = ImageDataGenerator( 63 | rescale=1. / 255, 64 | shear_range=0.2, 65 | zoom_range=0.2, 66 | horizontal_flip=True) 67 | 68 | test_datagen = ImageDataGenerator(rescale=1. / 255) 69 | 70 | train_generator = train_datagen.flow_from_directory( 71 | train_data_path, 72 | target_size=(img_height, img_width), 73 | batch_size=batch_size, 74 | class_mode='categorical') 75 | 76 | validation_generator = test_datagen.flow_from_directory( 77 | validation_data_path, 78 | target_size=(img_height, img_width), 79 | batch_size=batch_size, 80 | class_mode='categorical') 81 | 82 | """ 83 | Tensorboard log 84 | """ 85 | log_dir = './tf-log/' 86 | tb_cb = callbacks.TensorBoard(log_dir=log_dir, histogram_freq=0) 87 | cbks = [tb_cb] 88 | 89 | model.fit_generator( 90 | train_generator, 91 | samples_per_epoch=samples_per_epoch, 92 | epochs=epochs, 93 | validation_data=validation_generator, 94 | callbacks=cbks, 95 | validation_steps=validation_steps) 96 | 97 | target_dir = './models/' 98 | if not os.path.exists(target_dir): 99 | os.mkdir(target_dir) 100 | model.save('./models/model.h5') 101 | model.save_weights('./models/weights.h5') 102 | 103 | #Calculate execution time 104 | end = time.time() 105 | dur = end-start 106 | 107 | if dur<60: 108 | print("Execution Time:",dur,"seconds") 109 | elif dur>60 and dur<3600: 110 | dur=dur/60 111 | print("Execution Time:",dur,"minutes") 112 | else: 113 | dur=dur/(60*60) 114 | print("Execution Time:",dur,"hours") 115 | -------------------------------------------------------------------------------- /Using Tensorflow/dataset.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import os 3 | import glob 4 | from sklearn.utils import shuffle 5 | import numpy as np 6 | 7 | 8 | def load_train(train_path, image_size, classes): 9 | images = [] 10 | labels = [] 11 | img_names = [] 12 | cls = [] 13 | 14 | print('Going to read training images') 15 | for fields in classes: 16 | index = classes.index(fields) 17 | print('Now going to read {} files (Index: {})'.format(fields, index)) 18 | path = os.path.join(train_path, fields, '*g') 19 | files = glob.glob(path) 20 | for fl in files: 21 | image = cv2.imread(fl) 22 | image = cv2.resize(image, (image_size, image_size),0,0, cv2.INTER_LINEAR) 23 | image = image.astype(np.float32) 24 | image = np.multiply(image, 1.0 / 255.0) 25 | images.append(image) 26 | label = np.zeros(len(classes)) 27 | label[index] = 1.0 28 | labels.append(label) 29 | flbase = os.path.basename(fl) 30 | img_names.append(flbase) 31 | cls.append(fields) 32 | images = np.array(images) 33 | labels = np.array(labels) 34 | img_names = np.array(img_names) 35 | cls = np.array(cls) 36 | 37 | return images, labels, img_names, cls 38 | 39 | 40 | class DataSet(object): 41 | 42 | def __init__(self, images, labels, img_names, cls): 43 | self._num_examples = images.shape[0] 44 | 45 | self._images = images 46 | self._labels = labels 47 | self._img_names = img_names 48 | self._cls = cls 49 | self._epochs_done = 0 50 | self._index_in_epoch = 0 51 | 52 | @property 53 | def images(self): 54 | return self._images 55 | 56 | @property 57 | def labels(self): 58 | return self._labels 59 | 60 | @property 61 | def img_names(self): 62 | return self._img_names 63 | 64 | @property 65 | def cls(self): 66 | return self._cls 67 | 68 | @property 69 | def num_examples(self): 70 | return self._num_examples 71 | 72 | @property 73 | def epochs_done(self): 74 | return self._epochs_done 75 | 76 | def next_batch(self, batch_size): 77 | """Return the next `batch_size` examples from this data set.""" 78 | start = self._index_in_epoch 79 | self._index_in_epoch += batch_size 80 | 81 | if self._index_in_epoch > self._num_examples: 82 | # After each epoch we update this 83 | self._epochs_done += 1 84 | start = 0 85 | self._index_in_epoch = batch_size 86 | assert batch_size <= self._num_examples 87 | end = self._index_in_epoch 88 | 89 | return self._images[start:end], self._labels[start:end], self._img_names[start:end], self._cls[start:end] 90 | 91 | 92 | def read_train_sets(train_path, image_size, classes, validation_size): 93 | class DataSets(object): 94 | pass 95 | data_sets = DataSets() 96 | 97 | images, labels, img_names, cls = load_train(train_path, image_size, classes) 98 | images, labels, img_names, cls = shuffle(images, labels, img_names, cls) 99 | 100 | if isinstance(validation_size, float): 101 | validation_size = int(validation_size * images.shape[0]) 102 | 103 | validation_images = images[:validation_size] 104 | validation_labels = labels[:validation_size] 105 | validation_img_names = img_names[:validation_size] 106 | validation_cls = cls[:validation_size] 107 | 108 | train_images = images[validation_size:] 109 | train_labels = labels[validation_size:] 110 | train_img_names = img_names[validation_size:] 111 | train_cls = cls[validation_size:] 112 | 113 | data_sets.train = DataSet(train_images, train_labels, train_img_names, train_cls) 114 | data_sets.valid = DataSet(validation_images, validation_labels, validation_img_names, validation_cls) 115 | 116 | return data_sets 117 | -------------------------------------------------------------------------------- /Using Tensorflow/Prediction.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | import os 4 | import cv2 5 | import time 6 | 7 | start = time.time() 8 | 9 | try: 10 | 11 | # Path of training images 12 | train_path = r'C:\Users\T01144\Desktop\T01144\IDLE Scripts\Image Classification\Using Tensorflow\data\train' 13 | if not os.path.exists(train_path): 14 | print("No such directory") 15 | raise Exception 16 | # Path of testing images 17 | dir_path = r'C:\Users\T01144\Desktop\T01144\IDLE Scripts\Image Classification\Using Tensorflow\data\alien_test' 18 | if not os.path.exists(dir_path): 19 | print("No such directory") 20 | raise Exception 21 | 22 | # Walk though all testing images one by one 23 | for root, dirs, files in os.walk(dir_path): 24 | for name in files: 25 | 26 | print("") 27 | image_path = name 28 | filename = dir_path +'\\' +image_path 29 | print(filename) 30 | image_size=128 31 | num_channels=3 32 | images = [] 33 | 34 | if os.path.exists(filename): 35 | 36 | # Reading the image using OpenCV 37 | image = cv2.imread(filename) 38 | # Resizing the image to our desired size and preprocessing will be done exactly as done during training 39 | image = cv2.resize(image, (image_size, image_size),0,0, cv2.INTER_LINEAR) 40 | images.append(image) 41 | images = np.array(images, dtype=np.uint8) 42 | images = images.astype('float32') 43 | images = np.multiply(images, 1.0/255.0) 44 | 45 | # The input to the network is of shape [None image_size image_size num_channels]. Hence we reshape. 46 | x_batch = images.reshape(1, image_size,image_size,num_channels) 47 | 48 | # Let us restore the saved model 49 | sess = tf.Session() 50 | # Step-1: Recreate the network graph. At this step only graph is created. 51 | saver = tf.train.import_meta_graph('models/trained_model.meta') 52 | # Step-2: Now let's load the weights saved using the restore method. 53 | saver.restore(sess, tf.train.latest_checkpoint('./models/')) 54 | 55 | # Accessing the default graph which we have restored 56 | graph = tf.get_default_graph() 57 | 58 | # Now, let's get hold of the op that we can be processed to get the output. 59 | # In the original network y_pred is the tensor that is the prediction of the network 60 | y_pred = graph.get_tensor_by_name("y_pred:0") 61 | 62 | ## Let's feed the images to the input placeholders 63 | x= graph.get_tensor_by_name("x:0") 64 | y_true = graph.get_tensor_by_name("y_true:0") 65 | y_test_images = np.zeros((1, len(os.listdir(train_path)))) 66 | 67 | 68 | # Creating the feed_dict that is required to be fed to calculate y_pred 69 | feed_dict_testing = {x: x_batch, y_true: y_test_images} 70 | result=sess.run(y_pred, feed_dict=feed_dict_testing) 71 | # Result is of this format [[probabiliy_of_classA probability_of_classB ....]] 72 | print(result) 73 | 74 | # Convert np.array to list 75 | a = result[0].tolist() 76 | r=0 77 | 78 | # Finding the maximum of all outputs 79 | max1 = max(a) 80 | index1 = a.index(max1) 81 | predicted_class = None 82 | 83 | # Walk through directory to find the label of the predicted output 84 | count = 0 85 | for root, dirs, files in os.walk(train_path): 86 | for name in dirs: 87 | if count==index1: 88 | predicted_class = name 89 | count+=1 90 | 91 | # If the maximum confidence output is largest of all by a big margin then 92 | # print the class or else print a warning 93 | for i in a: 94 | if i!=max1: 95 | if max1-i60 and dur<3600: 117 | dur=dur/60 118 | print("Execution Time:",dur,"minutes") 119 | else: 120 | dur=dur/(60*60) 121 | print("Execution Time:",dur,"hours") 122 | -------------------------------------------------------------------------------- /Using Tensorflow/Training.py: -------------------------------------------------------------------------------- 1 | import dataset 2 | import tensorflow as tf 3 | import time 4 | import numpy as np 5 | import os 6 | 7 | start = time.time() 8 | try: 9 | 10 | # Total iterations 11 | final_iter = 1000 12 | 13 | # Assign the batch value 14 | batch_size = 20 15 | 16 | #20% of the data will automatically be used for validation 17 | validation_size = 0.2 18 | img_size = 128 19 | num_channels = 3 20 | train_path=r'C:\Users\T01144\Desktop\T01144\IDLE Scripts\Image Classification\Using Tensorflow\data\train' 21 | 22 | #Prepare input data 23 | if not os.path.exists(train_path): 24 | print("No such directory") 25 | raise Exception 26 | classes = os.listdir(train_path) 27 | num_classes = len(classes) 28 | 29 | # We shall load all the training and validation images and labels into memory using openCV and use that during training 30 | data = dataset.read_train_sets(train_path, img_size, classes, validation_size=validation_size) 31 | 32 | # Display the stats 33 | print("Complete reading input data. Will Now print a snippet of it") 34 | print("Number of files in Training-set:\t\t{}".format(len(data.train.labels))) 35 | print("Number of files in Validation-set:\t{}".format(len(data.valid.labels))) 36 | session = tf.Session() 37 | x = tf.placeholder(tf.float32, shape=[None, img_size,img_size,num_channels], name='x') 38 | 39 | ## labels 40 | y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true') 41 | y_true_cls = tf.argmax(y_true, dimension=1) 42 | 43 | 44 | ##Network graph params 45 | filter_size_conv1 = 3 46 | num_filters_conv1 = 32 47 | 48 | filter_size_conv2 = 3 49 | num_filters_conv2 = 32 50 | 51 | filter_size_conv3 = 3 52 | num_filters_conv3 = 64 53 | 54 | fc_layer_size = 128 55 | 56 | def create_weights(shape): 57 | return tf.Variable(tf.truncated_normal(shape, stddev=0.05)) 58 | 59 | def create_biases(size): 60 | return tf.Variable(tf.constant(0.05, shape=[size])) 61 | 62 | # Function to create a convolutional layer 63 | def create_convolutional_layer(input, 64 | num_input_channels, 65 | conv_filter_size, 66 | num_filters): 67 | 68 | ## We shall define the weights that will be trained using create_weights function. 69 | weights = create_weights(shape=[conv_filter_size, conv_filter_size, num_input_channels, num_filters]) 70 | ## We create biases using the create_biases function. These are also trained. 71 | biases = create_biases(num_filters) 72 | 73 | ## Creating the convolutional layer 74 | layer = tf.nn.conv2d(input=input, 75 | filter=weights, 76 | strides=[1, 1, 1, 1], 77 | padding='SAME') 78 | 79 | layer += biases 80 | 81 | ## We shall be using max-pooling. 82 | layer = tf.nn.max_pool(value=layer, 83 | ksize=[1, 2, 2, 1], 84 | strides=[1, 2, 2, 1], 85 | padding='SAME') 86 | ## Output of pooling is fed to Relu which is the activation function for us. 87 | layer = tf.nn.relu(layer) 88 | 89 | return layer 90 | 91 | 92 | # Function to create a Flatten Layer 93 | def create_flatten_layer(layer): 94 | #We know that the shape of the layer will be [batch_size img_size img_size num_channels] 95 | # But let's get it from the previous layer. 96 | layer_shape = layer.get_shape() 97 | 98 | ## Number of features will be img_height * img_width* num_channels. But we shall calculate it in place of hard-coding it. 99 | num_features = layer_shape[1:4].num_elements() 100 | 101 | ## Now, we Flatten the layer so we shall have to reshape to num_features 102 | layer = tf.reshape(layer, [-1, num_features]) 103 | 104 | return layer 105 | 106 | # Function to create a Fully - Connected Layer 107 | def create_fc_layer(input, 108 | num_inputs, 109 | num_outputs, 110 | use_relu=True): 111 | 112 | #Let's define trainable weights and biases. 113 | weights = create_weights(shape=[num_inputs, num_outputs]) 114 | biases = create_biases(num_outputs) 115 | 116 | # Fully connected layer takes input x and produces wx+b.Since, these are matrices, we use matmul function in Tensorflow 117 | layer = tf.matmul(input, weights) + biases 118 | if use_relu: 119 | layer = tf.nn.relu(layer) 120 | 121 | return layer 122 | 123 | # Create all the layers 124 | layer_conv1 = create_convolutional_layer(input=x, 125 | num_input_channels=num_channels, 126 | conv_filter_size=filter_size_conv1, 127 | num_filters=num_filters_conv1) 128 | layer_conv2 = create_convolutional_layer(input=layer_conv1, 129 | num_input_channels=num_filters_conv1, 130 | conv_filter_size=filter_size_conv2, 131 | num_filters=num_filters_conv2) 132 | 133 | layer_conv3= create_convolutional_layer(input=layer_conv2, 134 | num_input_channels=num_filters_conv2, 135 | conv_filter_size=filter_size_conv3, 136 | num_filters=num_filters_conv3) 137 | 138 | layer_flat = create_flatten_layer(layer_conv3) 139 | 140 | layer_fc1 = create_fc_layer(input=layer_flat, 141 | num_inputs=layer_flat.get_shape()[1:4].num_elements(), 142 | num_outputs=fc_layer_size, 143 | use_relu=True) 144 | 145 | layer_fc2 = create_fc_layer(input=layer_fc1, 146 | num_inputs=fc_layer_size, 147 | num_outputs=num_classes, 148 | use_relu=False) 149 | 150 | y_pred = tf.nn.softmax(layer_fc2,name='y_pred') 151 | 152 | y_pred_cls = tf.argmax(y_pred, dimension=1) 153 | session.run(tf.global_variables_initializer()) 154 | cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=layer_fc2, 155 | labels=y_true) 156 | cost = tf.reduce_mean(cross_entropy) 157 | optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost) 158 | correct_prediction = tf.equal(y_pred_cls, y_true_cls) 159 | accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 160 | 161 | 162 | session.run(tf.global_variables_initializer()) 163 | 164 | # Display all stats for every epoch 165 | def show_progress(epoch, feed_dict_train, feed_dict_validate, val_loss,total_epochs): 166 | acc = session.run(accuracy, feed_dict=feed_dict_train) 167 | val_acc = session.run(accuracy, feed_dict=feed_dict_validate) 168 | msg = "Training Epoch {0}/{4} --- Training Accuracy: {1:>6.1%}, Validation Accuracy: {2:>6.1%}, Validation Loss: {3:.3f}" 169 | print(msg.format(epoch + 1, acc, val_acc, val_loss,total_epochs)) 170 | 171 | total_iterations = 0 172 | 173 | saver = tf.train.Saver() 174 | 175 | print("") 176 | 177 | # Training Function 178 | def train(num_iteration): 179 | global total_iterations 180 | 181 | for i in range(total_iterations, 182 | total_iterations + num_iteration): 183 | 184 | x_batch, y_true_batch, _, cls_batch = data.train.next_batch(batch_size) 185 | x_valid_batch, y_valid_batch, _, valid_cls_batch = data.valid.next_batch(batch_size) 186 | 187 | 188 | feed_dict_tr = {x: x_batch, 189 | y_true: y_true_batch} 190 | feed_dict_val = {x: x_valid_batch, 191 | y_true: y_valid_batch} 192 | 193 | session.run(optimizer, feed_dict=feed_dict_tr) 194 | 195 | if i % int(data.train.num_examples/batch_size) == 0: 196 | val_loss = session.run(cost, feed_dict=feed_dict_val) 197 | epoch = int(i / int(data.train.num_examples/batch_size)) 198 | #print(data.train.num_examples) 199 | #print(batch_size) 200 | #print(int(data.train.num_examples/batch_size)) 201 | #print(i) 202 | 203 | total_epochs = int(num_iteration/int(data.train.num_examples/batch_size)) + 1 204 | show_progress(epoch, feed_dict_tr, feed_dict_val, val_loss,total_epochs) 205 | saver.save(session, 'C:\\Users\\T01144\\Desktop\\T01144\\IDLE Scripts\\Image Classification\\Using Tensorflow\\models\\trained_model') 206 | 207 | total_iterations += num_iteration 208 | 209 | train(num_iteration = final_iter) 210 | 211 | except Exception as e: 212 | print("Exception:",e) 213 | 214 | # Calculate execution time 215 | end = time.time() 216 | dur = end-start 217 | print("") 218 | if dur<60: 219 | print("Execution Time:",dur,"seconds") 220 | elif dur>60 and dur<3600: 221 | dur=dur/60 222 | print("Execution Time:",dur,"minutes") 223 | else: 224 | dur=dur/(60*60) 225 | print("Execution Time:",dur,"hours") 226 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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