def build_model_anything_happening(): ### IS ANY OF THIS NECESSARY FOR LIGHT/DARK? IN GENERAL W/ STAIONARY CAMERA? img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() img_aug = ImageAugmentation() img_aug.add_random_flip_leftright() # Specify shape of the data, image prep network = input_data(shape=[None, 52, 64], data_preprocessing=img_prep, data_augmentation=img_aug) # Since the image position remains consistent and are fairly similar, this can be spatially aware. # Using a fully connected network directly, no need for convolution. network = fully_connected(network, 2048, activation='relu') network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.00003) model = tflearn.DNN(network, tensorboard_verbose=0) return model
def _model1(): global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() network = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) network = conv_2d(network, 32, 3, strides = 4, activation='relu') network = max_pool_2d(network, 2, strides=2) network = local_response_normalization(network) network = conv_2d(network, 64, 3, strides = 2, activation='relu') network = max_pool_2d(network, 2, strides=2) network = local_response_normalization(network) network = fully_connected(network, 128, activation='tanh') network = dropout(network, 0.8) network = fully_connected(network, 256, activation='tanh') network = dropout(network, 0.8) network = fully_connected(network, len(Y[0]), activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') model = tflearn.DNN(network, tensorboard_verbose=3) model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest), snapshot_step=500, show_metric=True, batch_size=batchNum, shuffle=True, run_id=_id + 'artClassification') if modelStore: model.save(_id + '-model.tflearn')
def _model2(): global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() net = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) n = 2 j = 64 ''' net = tflearn.conv_2d(net, j, 3, regularizer='L2', weight_decay=0.0001) net = tflearn.residual_block(net, n, j) net = tflearn.residual_block(net, 1, j*2, downsample=True) net = tflearn.residual_block(net, n-1, j*2) net = tflearn.residual_block(net, 1, j*4, downsample=True) net = tflearn.residual_block(net, n-1, j*4) net = tflearn.residual_block(net, 1, j*8, downsample=True) net = tflearn.residual_block(net, n-1, j*8) net = tflearn.batch_normalization(net) net = tflearn.activation(net, 'relu') net = tflearn.global_avg_pool(net) ''' net = tflearn.conv_2d(net, j, 7, strides = 2, regularizer='L2', weight_decay=0.0001) net = max_pool_2d(net, 2, strides=2) net = tflearn.residual_block(net, n, j) net = tflearn.residual_block(net, 1, j*2, downsample=True) net = tflearn.residual_block(net, n-1, j*2) net = tflearn.residual_block(net, 1, j*4, downsample=True) net = tflearn.residual_block(net, n-1, j*4) net = tflearn.residual_block(net, 1, j*8, downsample=True) net = tflearn.residual_block(net, n-1, j*8) net = tflearn.batch_normalization(net) net = tflearn.activation(net, 'relu') net = tflearn.global_avg_pool(net) net = tflearn.fully_connected(net, len(yTest[0]), activation='softmax') mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True) net = tflearn.regression(net, optimizer=mom, loss='categorical_crossentropy') model = tflearn.DNN(net, checkpoint_path='model2_resnet', max_checkpoints=10, tensorboard_verbose=3, clip_gradients=0.) model.load(_path) pred = model.predict(xTest) df = pd.DataFrame(pred) df.to_csv(_path + ".csv") newList = pred.copy() newList = convert2(newList) if _CSV: makeCSV(newList) pred = convert2(pred) pred = convert3(pred) yTest = convert3(yTest) print(metrics.confusion_matrix(yTest, pred)) print(metrics.classification_report(yTest, pred)) print('Accuracy', accuracy_score(yTest, pred)) print() if _wrFile: writeTest(pred)
def run(self): # Real-time pre-processing of the image data img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Real-time data augmentation img_aug = tflearn.ImageAugmentation() img_aug.add_random_flip_leftright() # Resnet model below: Adapted from tflearn website self.n = 5 #32 layer resnet # Building Residual Network net = tflearn.input_data(shape=[None, 48, 48, 1], data_preprocessing=img_prep, data_augmentation=img_aug) net = tflearn.conv_2d(net, nb_filter=16, filter_size=3, regularizer='L2', weight_decay=0.0001) net = tflearn.residual_block(net, self.n, 16) net = tflearn.residual_block(net, 1, 32, downsample=True) net = tflearn.residual_block(net, self.n - 1, 32) net = tflearn.residual_block(net, 1, 64, downsample=True) net = tflearn.residual_block(net, self.n - 1, 64) net = tflearn.batch_normalization(net) net = tflearn.activation(net, 'relu') net = tflearn.global_avg_pool(net) # Regression net = tflearn.fully_connected(net, 7, activation='softmax') mom = tflearn.Momentum(learning_rate=0.1, lr_decay=0.0001, decay_step=32000, staircase=True, momentum=0.9) net = tflearn.regression(net, optimizer=mom, loss='categorical_crossentropy') self.model = tflearn.DNN(net, checkpoint_path='models/model_resnet_emotion', max_checkpoints=10, tensorboard_verbose=0, clip_gradients=0.) self.model.load('model.tfl') face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') cap = cv2.VideoCapture(0) #Main Loop where we will be capturing live webcam feed, crop image and process the image for emotion recognition on trained model while True: ret, img = cap.read() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2) roi_gray = gray[y:y + h, x:x + w] roi_color = img[y:y + h, x:x + w] self.image_processing(roi_gray, img) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows()
def _model3(): global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() network = input_data(shape=[None, inputSize, inputSize, dim], data_preprocessing=img_prep, data_augmentation=img_aug) network = conv_2d(network, 96, 11, strides=4, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 256, 5, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 256, 3, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, len(yTest[0]), activation='softmax') network = regression(network, optimizer='momentum', loss='categorical_crossentropy', learning_rate=0.001) print('Model has been made!!!?') # Training model = tflearn.DNN(network, checkpoint_path='model_densenet_cifar10', max_checkpoints=10, tensorboard_verbose=0, clip_gradients=0.) model.load(_path) pred = model.predict(xTest) df = pd.DataFrame(pred) df.to_csv(_path + ".csv") newList = pred.copy() newList = convert2(newList) if _CSV: makeCSV(newList) pred = convert2(pred) pred = convert3(pred) yTest = convert3(yTest) print(metrics.confusion_matrix(yTest, pred)) print(metrics.classification_report(yTest, pred)) print('Accuracy', accuracy_score(yTest, pred)) print() if _wrFile: writeTest(pred)
def generate_network(self): """ Return tflearn cnn network. """ print(self.image_size, self.n_epoch, self.batch_size, self.person_ids) print(type(self.image_size), type(self.n_epoch), type(self.batch_size), type(self.person_ids)) if not isinstance(self.image_size, list) \ or not isinstance(self.n_epoch, int) \ or not isinstance(self.batch_size, int) \ or not isinstance(self.person_ids, list): # if self.image_size is None or self.n_epoch is None or \ # self.batch_size is None or self.person_ids is None: raise ValueError("Insufficient values to generate network.\n" "Need (n_epoch, int), (batch_size, int)," "(image_size, list), (person_ids, list).") # Real-time data preprocessing img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Real-time data augmentation img_aug = ImageAugmentation() img_aug.add_random_rotation(max_angle=25.) img_aug.add_random_flip_leftright() # Convolutional network building network = input_data( shape=[None, self.image_size[0], self.image_size[1], 3], data_preprocessing=img_prep, data_augmentation=img_aug) network = conv_2d(network, self.image_size[0], self.IMAGE_CHANNEL_NUM, activation='relu') network = max_pool_2d(network, 2) network = conv_2d(network, self.image_size[0] * 2, self.IMAGE_CHANNEL_NUM, activation='relu') network = conv_2d(network, self.image_size[0] * 2, self.IMAGE_CHANNEL_NUM, activation='relu') network = max_pool_2d(network, 2) network = fully_connected(network, self.image_size[0] * 2**4, activation='relu') network = dropout(network, 0.5) network = fully_connected(network, self.person_num, activation='softmax') network = regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001) return network
def setup_model(checkpoint_path=None): """Sets up a deep belief network for image classification based on the set up described in :param checkpoint_path: string path describing prefix for model checkpoints :returns: Deep Neural Network :rtype: tflearn.DNN References: - Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks Links: - https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721 """ # Make sure the data is normalized img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Create extra synthetic training data by flipping, rotating and blurring the # images on our data set. img_aug = ImageAugmentation() img_aug.add_random_flip_leftright() img_aug.add_random_rotation(max_angle=25.) img_aug.add_random_blur(sigma_max=3.) # Input is a 32x32 image with 3 color channels (red, green and blue) network = input_data(shape=[None, 32, 32, 3], data_preprocessing=img_prep, data_augmentation=img_aug) network = conv_2d(network, 32, 3, activation='relu') network = max_pool_2d(network, 2) network = conv_2d(network, 64, 3, activation='relu') network = conv_2d(network, 64, 3, activation='relu') network = max_pool_2d(network, 2) network = fully_connected(network, 512, activation='relu') network = dropout(network, 0.5) network = fully_connected(network, 2, activation='softmax') network = regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001) if checkpoint_path: model = tflearn.DNN(network, tensorboard_verbose=3, checkpoint_path=checkpoint_path) else: model = tflearn.DNN(network, tensorboard_verbose=3) return model
def _model2(): global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() net = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) n = 3 j = 64 ''' net = tflearn.conv_2d(net, j, 3, regularizer='L2', weight_decay=0.0001) net = tflearn.residual_block(net, n, j) net = tflearn.residual_block(net, 1, j*2, downsample=True) net = tflearn.residual_block(net, n-1, j*2) net = tflearn.residual_block(net, 1, j*4, downsample=True) net = tflearn.residual_block(net, n-1, j*4) net = tflearn.residual_block(net, 1, j*8, downsample=True) net = tflearn.residual_block(net, n-1, j*8) net = tflearn.batch_normalization(net) net = tflearn.activation(net, 'relu') net = tflearn.global_avg_pool(net) ''' net = tflearn.conv_2d(net, j, 7, strides = 2, regularizer='L2', weight_decay=0.0001) net = max_pool_2d(net, 2, strides=2) net = tflearn.residual_block(net, n, j) net = tflearn.residual_block(net, 1, j*2, downsample=True) net = tflearn.residual_block(net, n-1, j*2) net = tflearn.residual_block(net, 1, j*4, downsample=True) net = tflearn.residual_block(net, n-1, j*4) net = tflearn.residual_block(net, 1, j*8, downsample=True) net = tflearn.residual_block(net, n-1, j*8) net = tflearn.batch_normalization(net) net = tflearn.activation(net, 'relu') net = tflearn.global_avg_pool(net) net = tflearn.fully_connected(net, len(Y[0]), activation='softmax') mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True) net = tflearn.regression(net, optimizer=mom, loss='categorical_crossentropy') model = tflearn.DNN(net, checkpoint_path='model_resnet_cifar10', max_checkpoints=10, tensorboard_verbose=3, clip_gradients=0.) model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest),snapshot_epoch=False, snapshot_step=500, show_metric=True, batch_size=batchNum, shuffle=True, run_id= _id + 'artClassification') if modelStore: model.save(_id + '-model.tflearn')
def _model1(): global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() network = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) network = conv_2d(network, 32, 3, strides = 4, activation='relu') network = max_pool_2d(network, 2, strides=2) network = local_response_normalization(network) network = conv_2d(network, 64, 3, strides = 2, activation='relu') network = max_pool_2d(network, 2, strides=2) network = local_response_normalization(network) network = fully_connected(network, 128, activation='tanh') network = dropout(network, 0.8) network = fully_connected(network, 256, activation='tanh') network = dropout(network, 0.8) network = fully_connected(network, len(yTest[0]), activation='softmax') network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='target') model = tflearn.DNN(network, tensorboard_verbose=3) model.load(_path) pred = model.predict(xTest) df = pd.DataFrame(pred) df.to_csv(_path + ".csv") newList = pred.copy() newList = convert2(newList) if _CSV: makeCSV(newList) pred = convert2(pred) pred = convert3(pred) yTest = convert3(yTest) print(metrics.confusion_matrix(yTest, pred)) print(metrics.classification_report(yTest, pred)) print('Accuracy', accuracy_score(yTest, pred)) print() if _wrFile: writeTest(pred)
def build_model_specific(): ### IS ANY OF THIS NECESSARY FOR LIGHT/DARK? IN GENERAL W/ STAIONARY CAMERA? img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() img_aug = ImageAugmentation() img_aug.add_random_flip_leftright() # Specify shape of the data, image prep network = input_data(shape=[None, 52, 64], data_preprocessing=img_prep, data_augmentation=img_aug) # conv_2d incoming, nb_filter, filter_size # incoming: Tensor. Incoming 4-D Tensor. # nb_filter: int. The number of convolutional filters. # WHAT IS THIS? # filter_size: 'intor list ofints`. Size of filters. # WHAT IS THIS? network = conv_1d(network, 512, 3, activation='relu') # (incoming, kernel_size) # incoming: Tensor. Incoming 4-D Layer. # kernel_size: 'intor list ofints`. Pooling kernel size. network = max_pool_1d(network, 2) network = conv_1d(network, 64, 3, activation='relu') network = conv_1d(network, 64, 3, activation='relu') network = max_pool_1d(network, 2) network = fully_connected(network, 512, activation='relu') network = dropout(network, 0.5) network = fully_connected(network, 4, activation='softmax') network = regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.0003) model = tflearn.DNN(network, tensorboard_verbose=0) return model
def _model3(): global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() network = input_data(shape=[None, inputSize, inputSize, dim], data_preprocessing=img_prep, data_augmentation=img_aug) network = conv_2d(network, 96, 11, strides=4, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 256, 5, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 256, 3, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, len(Y[0]), activation='softmax') network = regression(network, optimizer='momentum', loss='categorical_crossentropy', learning_rate=0.001) print('Model has been made!!!?') # Training model = tflearn.DNN(network, checkpoint_path='model_densenet_cifar10', max_checkpoints=10, tensorboard_verbose=0, clip_gradients=0.) model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest), snapshot_epoch=False, snapshot_step=200, show_metric=True, batch_size=batchNum, shuffle=True, run_id='resnext_cifar10') if modelStore: model.save(_id + '-model.tflearn')
if (comm_multi_label_scenario == 1): XTrain, YTrain, num_output_classes = ssld.load_multi_label_data( comm_train_text_file_ML, comm_root_image_path_ML, (cnn_image_shape_before_crop[0], cnn_image_shape_before_crop[1]), normalize=True, grayscale=False) XVal, YVal, num_output_classes = ssld.load_multi_label_data( comm_val_text_file_ML, comm_root_image_path_ML, (cnn_image_shape_before_crop[0], cnn_image_shape_before_crop[1]), normalize=True, grayscale=False) # Image Preprocessing Methods Instantiation cnn_img_prep = ImagePreprocessing() if (cnn_compute_mean_std == 1): cnn_img_prep.add_featurewise_zero_center( ) # Zero Center (With mean computed over the whole dataset) cnn_img_prep.add_featurewise_stdnorm( ) # STD Normalization (With std computed over the whole dataset) if (cnn_compute_mean_std == 0): cnn_img_prep.add_featurewise_zero_center(per_channel=True, mean=cnn_mean_vector_precomputed) cnn_img_prep.add_featurewise_stdnorm(per_channel=True, std=cnn_std_vector_precomputed) cnn_img_aug = tflearn.ImageAugmentation() # Real-time data augmentation cnn_img_aug.add_random_flip_leftright() # Random flip an image cnn_img_aug.add_random_crop([cnn_image_shape[0], cnn_image_shape[1]])
files_list = "/path/to/your/file/with/images" from tflearn.data_utils import image_preloader X, Y = image_preloader(files_list, image_shape=(224, 224), mode='file', categorical_labels=True, normalize=False, files_extension=['.jpg', '.png'], filter_channel=True) # or use the mode 'floder' # X, Y = image_preloader(data_dir, image_shape=(224, 224), mode='folder', # categorical_labels=True, normalize=True, # files_extension=['.jpg', '.png'], filter_channel=True) num_classes = 10 # num of your dataset # VGG preprocessing img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center(mean=[123.68, 116.779, 103.939], per_channel=True) # VGG Network x = tflearn.input_data(shape=[None, 224, 224, 3], name='input', data_preprocessing=img_prep) softmax = vgg16(x, num_classes) regression = tflearn.regression(softmax, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001, restore=False) model = tflearn.DNN(regression, checkpoint_path='vgg-finetuning', max_checkpoints=3, tensorboard_verbose=2, tensorboard_dir="./logs") model_file = os.path.join(model_path, "vgg16.tflearn")
def _model5(): global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() def block35(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 32, 3, bias=False, activation=None,name='Conv2d_0b_3x3'))) tower_conv2_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 48,3, bias=False, activation=None, name='Conv2d_0b_3x3'))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 64,3, bias=False, activation=None, name='Conv2d_0c_3x3'))) tower_mixed = merge([tower_conv, tower_conv1_1, tower_conv2_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net def block17(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv_1_0 = relu(batch_normalization(conv_2d(net, 128, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv_1_1 = relu(batch_normalization(conv_2d(tower_conv_1_0, 160,[1,7], bias=False, activation=None,name='Conv2d_0b_1x7'))) tower_conv_1_2 = relu(batch_normalization(conv_2d(tower_conv_1_1, 192, [7,1], bias=False, activation=None,name='Conv2d_0c_7x1'))) tower_mixed = merge([tower_conv,tower_conv_1_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net def block8(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 224, [1,3], bias=False, activation=None, name='Conv2d_0b_1x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 256, [3,1], bias=False, name='Conv2d_0c_3x1'))) tower_mixed = merge([tower_conv,tower_conv1_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net num_classes = len(yTest[0]) dropout_keep_prob = 0.8 network = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) conv1a_3_3 = relu(batch_normalization(conv_2d(network, 32, 3, strides=2, bias=False, padding='VALID',activation=None,name='Conv2d_1a_3x3'))) conv2a_3_3 = relu(batch_normalization(conv_2d(conv1a_3_3, 32, 3, bias=False, padding='VALID',activation=None, name='Conv2d_2a_3x3'))) conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3'))) maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3') conv3b_1_1 = relu(batch_normalization(conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID',activation=None, name='Conv2d_3b_1x1'))) conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3'))) maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3') tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5'))) tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3'))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3'))) tower_pool3_0 = avg_pool_2d(maxpool5a_3_3, 3, strides=1, padding='same', name='AvgPool_5b_b3_0a_3x3') tower_conv3_1 = relu(batch_normalization(conv_2d(tower_pool3_0, 64, 1, bias=False, activation=None,name='Conv2d_5b_b3_0b_1x1'))) tower_5b_out = merge([tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1], mode='concat', axis=3) net = repeat(tower_5b_out, 10, block35, scale=0.17) ''' tower_conv = relu(batch_normalization(conv_2d(net, 384, 3, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 3, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3'))) tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID',name='MaxPool_1a_3x3') net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3) net = repeat(net, 20, block17, scale=0.1) tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,3, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3'))) tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,3, bias=False, name='Conv2d_0b_3x3',activation=None))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 3, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3'))) tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID', name='MaxPool_1a_3x3') ''' tower_conv = relu(batch_normalization(conv_2d(net, 384, 1, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3'))) tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID',name='MaxPool_1a_3x3') net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3) net = repeat(net, 20, block17, scale=0.1) tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,1, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3'))) tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,1, bias=False, name='Conv2d_0b_3x3',activation=None))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 1, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3'))) tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID', name='MaxPool_1a_3x3') #### net = merge([tower_conv0_1, tower_conv1_1,tower_conv2_2, tower_pool], mode='concat', axis=3) net = repeat(net, 9, block8, scale=0.2) net = block8(net, activation=None) net = relu(batch_normalization(conv_2d(net, 1536, 1, bias=False, activation=None, name='Conv2d_7b_1x1'))) net = avg_pool_2d(net, net.get_shape().as_list()[1:3],strides=2, padding='VALID', name='AvgPool_1a_8x8') net = flatten(net) net = dropout(net, dropout_keep_prob) loss = fully_connected(net, num_classes,activation='softmax') network = tflearn.regression(loss, optimizer='RMSprop', loss='categorical_crossentropy', learning_rate=0.0001) model = tflearn.DNN(network, checkpoint_path='inception_resnet_v2', max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir="./tflearn_logs/") model.load(_path) pred = model.predict(xTest) df = pd.DataFrame(pred) df.to_csv(_path + ".csv") newList = pred.copy() newList = convert2(newList) if _CSV: makeCSV(newList) pred = convert2(pred) pred = convert3(pred) yTest = convert3(yTest) print(metrics.confusion_matrix(yTest, pred)) print(metrics.classification_report(yTest, pred)) print('Accuracy', accuracy_score(yTest, pred)) print() if _wrFile: writeTest(pred)
index = int(tmp[1]) label = np.zeros(num_clss) label[index] = 1 labels.append(label) if save: pickle.dump((images, labels), open(save_path, 'wb')) return images, labels def load_from_pkl(dataset_file): X, Y = pickle.load(open(dataset_file, 'rb')) return X, Y # Make sure the data is normalized img_prep = ImagePreprocessing() # img_prep.add_featurewise_zero_center(31.83) # img_prep.add_featurewise_stdnorm(50.96) # Create extra synthetic training data by flipping, rotating and blurring the # images on our data set. img_aug = ImageAugmentation() # img_aug.add_random_flip_leftright() #img_aug.add_random_crop((227, 227), 20) img_aug.add_random_rotation(max_angle=180.) def create_alexnet(num_classes): # Building 'AlexNet' network = input_data(shape=[None, 227, 227, 3], data_preprocessing=img_prep,
def _model4(): # Taken from TFLearn examples and based on Googles Inception. DO NOT RUN!!!! global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() network = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2') pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2) pool1_3_3 = local_response_normalization(pool1_3_3) conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce') conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3') conv2_3_3 = local_response_normalization(conv2_3_3) pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2') # 3a inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1') inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce') inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3') inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce') inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5') inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool') inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1') inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3) # 3b inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1') inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce') inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3') inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce') inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5') inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool') inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1') inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output') pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3') # 4a inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce') inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3') inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce') inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5') inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool') inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1') inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output') # 4b inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce') inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3') inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce') inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5') inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool') inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1') inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output') # 4c inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1') inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce') inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3') inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce') inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5') inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1) inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1') inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output') # 4d inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1') inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce') inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3') inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce') inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5') inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool') inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1') inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output') # 4e inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1') inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce') inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3') inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce') inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5') inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool') inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1') inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat') pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3') # 5a inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1') inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce') inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3') inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce') inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5') inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool') inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1') inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat') # 5b inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1') inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce') inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3') inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce') inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5') inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool') inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1') inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat') pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1) pool5_7_7 = dropout(pool5_7_7, 0.4) # fc loss = fully_connected(pool5_7_7, len(Y[0]), activation='softmax') network = regression(loss, optimizer='momentum', loss='categorical_crossentropy', learning_rate=0.001) # to train model = tflearn.DNN(network, checkpoint_path='model_googlenet', max_checkpoints=1, tensorboard_verbose=2) model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest), shuffle=True, show_metric=True, batch_size=batchNum, snapshot_step=200, snapshot_epoch=False, run_id='googlenet_oxflowers17') if modelStore: model.save(_id + '-model.tflearn')
print(Y.shape) #Reshaping the Numpy Arrays X = X.reshape([-1, 256, 256, 3]) X_test = X_test.reshape([-1, 256, 256, 3]) Y = to_categorical(Y, 3) Y_test = to_categorical(Y_test, 3) print("Starting Data Manipulation...") # Shuffle the data print("Shuffling the Data...") # Shuffle the data X, Y = shuffle(X, Y) # Make sure the data is normalized img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Create extra synthetic training data by flipping, rotating and blurring the # images on our data set. img_aug = ImageAugmentation() img_aug.add_random_flip_leftright() img_aug.add_random_rotation(max_angle=25.) img_aug.add_random_blur(sigma_max=3.) # Define our network architecture: # Input is a 32x32 image with 3 color channels (red, green and blue) network = input_data(shape=[None, 256, 256, 3], data_preprocessing=img_prep,
X, Y = image_preloader(files_list, image_shape=(224, 224), mode='file', categorical_labels=True, normalize=False, files_extension=['.jpg', '.png'], filter_channel=True) # or use the mode 'floder' # X, Y = image_preloader(data_dir, image_shape=(224, 224), mode='folder', # categorical_labels=True, normalize=True, # files_extension=['.jpg', '.png'], filter_channel=True) num_classes = 10 # num of your dataset # VGG preprocessing img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center(mean=[123.68, 116.779, 103.939], per_channel=True) # VGG Network x = tflearn.input_data(shape=[None, 224, 224, 3], name='input', data_preprocessing=img_prep) softmax = vgg16(x, num_classes) regression = tflearn.regression(softmax, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001, restore=False) model = tflearn.DNN(regression, checkpoint_path='vgg-finetuning',
from tflearn.data_augmentation import ImageAugmentation from tflearn.data_utils import image_preloader # Data Loading and Categorization runbook = 'data/mowmowSanity.csv' X, Y = image_preloader(runbook, image_shape=(128, 128), mode='file', categorical_labels=False, normalize=False) Y = to_categorical(Y, 4) print(Y) print("-- Runbook Import Complete.") # Real-time Data Preprocessing img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center(mean=110.53345696347691) img_prep.add_featurewise_stdnorm(std=30.956539195568315) print("-- Data Preprocessing Complete.") # Real-time Data Augmentation img_aug = ImageAugmentation() img_aug.add_random_flip_leftright() img_aug.add_random_rotation(max_angle=25.) print("-- Data Augmentation Complete.") # Convolutional Network network = input_data(shape=[None, 128, 128, 3], data_preprocessing=img_prep, data_augmentation=img_aug) network = conv_2d(network,
y_test = y_test.reshape(-1) # In[16]: y_train_ohc = pd.get_dummies(y_train) y_test_ohc = pd.get_dummies(y_test) # In[17]: # shuffling the data X_train, y_train = shuffle(X_train, y_train) # In[18]: # normalizing img_pre = ImagePreprocessing() img_pre.add_featurewise_zero_center(33.3184) img_pre.add_featurewise_stdnorm(78.56748998339795) # In[19]: # image augmentation img_aug = ImageAugmentation() img_aug.add_random_flip_leftright() img_aug.add_random_rotation(max_angle=25.) img_aug.add_random_blur(sigma_max=3.) # ## Network Architecture # In[20]:
def network1(train, train_amount): global model # region SPLIT DATA FOR TRAIN/VALIDATION train_amount = int(train_amount * 5.5 / 6) x_train = np.array([i[0] for i in train[:train_amount] ]).reshape(-1, s.IMG_SIZE, s.IMG_SIZE, 1) x_train = x_train / 255.0 y_train = [i[1] for i in train[:train_amount]] x_validation = np.array([i[0] for i in train[train_amount:] ]).reshape(-1, s.IMG_SIZE, s.IMG_SIZE, 1) x_validation = x_validation / 255.0 y_validation = [i[1] for i in train[train_amount:]] # endregion # region NETWORK img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center(mean=[0.4735053442384178]) network = input_data(shape=[None, s.IMG_SIZE, s.IMG_SIZE, 1], name='input', data_preprocessing=img_prep) network = conv_2d(network, 32, 3, activation='relu', scope='conv1_1') network = conv_2d(network, 64, 3, activation='relu', scope='conv1_2') network = max_pool_2d(network, 2, strides=2, name='maxpool_1') network = conv_2d(network, 128, 3, activation='relu', scope='conv2_1') network = max_pool_2d(network, 2, strides=2, name='maxpool_2') network = conv_2d(network, 128, 3, activation='relu', scope='conv3_1') network = max_pool_2d(network, 2, strides=2, name='maxpool_3') network = conv_2d(network, 256, 3, activation='relu', scope='conv4_1') network = max_pool_2d(network, 2, strides=2, name='maxpool_4') network = fully_connected(network, 1024, activation='relu', scope='fc5') network = dropout(network, 0.5, name='dropout_1') network = fully_connected(network, 1024, activation='relu', scope='fc6') network = dropout(network, 0.5, name='dropout_2') network = fully_connected(network, s.len_animals, activation='softmax', scope='fc7') network = regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=s.LR, name='targets') model = tflearn.DNN(network, tensorboard_verbose=0, tensorboard_dir='log') # endregion if os.path.exists('{}.meta'.format(s.MODEL_NAME)): model.load(s.MODEL_NAME) print('Model loaded') # region TRAIN1 model.fit(x_train, y_train, n_epoch=12, validation_set=({ 'input': x_validation }, { 'targets': y_validation }), shuffle=True, snapshot_epoch=True, show_metric=True, batch_size=100, run_id=s.MODEL_NAME) # endregion # region SAVE model.save(s.MODEL_NAME) print('Network trained and saved as {0}'.format(s.MODEL_NAME))
def run(self): # Real-time pre-processing of the image data img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Real-time data augmentation img_aug = tflearn.ImageAugmentation() img_aug.add_random_flip_leftright() # img_aug.add_random_crop([48, 48], padding=8) # Building Residual Network net = tflearn.input_data(shape=[None, 48, 48, 1], data_preprocessing=img_prep, data_augmentation=img_aug) net = tflearn.conv_2d(net, nb_filter=16, filter_size=3, regularizer='L2', weight_decay=0.0001) net = tflearn.residual_block(net, self.n, 16) net = tflearn.residual_block(net, 1, 32, downsample=True) net = tflearn.residual_block(net, self.n - 1, 32) net = tflearn.residual_block(net, 1, 64, downsample=True) net = tflearn.residual_block(net, self.n - 1, 64) net = tflearn.batch_normalization(net) net = tflearn.activation(net, 'relu') net = tflearn.global_avg_pool(net) # Regression net = tflearn.fully_connected(net, 7, activation='softmax') mom = tflearn.Momentum(learning_rate=0.1, lr_decay=0.0001, decay_step=32000, staircase=True, momentum=0.9) net = tflearn.regression(net, optimizer=mom, loss='categorical_crossentropy') self.model = tflearn.DNN(net, checkpoint_path='models/model_resnet_emotion', max_checkpoints=10, tensorboard_verbose=0, clip_gradients=0.) self.model.load('current_model/model_resnet_emotion-42000') face_cascade = cv2.CascadeClassifier( 'haarcascade_frontalface_default.xml') cap = cv2.VideoCapture(0) while True: ret, img = cap.read() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2) roi_gray = gray[y:y + h, x:x + w] roi_color = img[y:y + h, x:x + w] self.process_image(roi_gray, img) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows()
def color(): ################################### ### Import picture files ################################### files_path = "./img_align_celeba/" glasses_files_path = os.path.join(files_path, '*.jpg') glasses_files = sorted(glob(glasses_files_path)) n_files = len(glasses_files) #print(n_files) #size_image1 =178 #size_image2=218 size_image1=27 size_image2=33 allX = np.zeros((n_files, size_image1, size_image2, 3), dtype='float32') ally = np.zeros(n_files) count = 0 for f in glasses_files: try: img = io.imread(f) new_img = skimage.transform.resize(img, (27, 33, 3)) allX[count] = np.array(new_img) ally[count] = 0 count += 1 except: continue attribute=[] g = open('./list_attr_celeba.txt', 'r') text = g.readlines() text = np.array(text) attr2idx = dict() for i, attr in enumerate(text[1].split()): attr2idx[attr] = i attr_index = attr2idx['Eyeglasses']#'Eyeglasses' for i in text[2:]: value = i.split() attribute.append(value[attr_index + 1]) #First index is image name attribute = np.array(attribute,dtype= np.float32) #print("Converting Label.................") for i in range(0,len(attribute)): if (attribute[i] == 1): ally[i]=1 else: ally[i]=0 ally = np.array(ally) ########break up data into training, validation, and test sets train_limit = int(math.floor(0.8 * len(allX))) validate_limit = int(math.floor(0.1*len(allX))) #print (train_limit, validate_limit) X = allX[0:train_limit,:,] X_validation = allX[(train_limit+1):(train_limit+validate_limit),:,] X_test = allX[(train_limit+validate_limit+1):,:,] Y = ally[0:train_limit] Y_validation = ally[(train_limit+1):(train_limit+validate_limit)] Y_test = ally[(train_limit+validate_limit+1):] # encode the Ys Y = to_categorical(Y, 2) Y_test = to_categorical(Y_test, 2) Y_validation = to_categorical(Y_validation, 2) #take a subset of training dataset to find parameters x_sm = int(math.floor(0.8 * len(allX))*0.5) print (x_sm) X_sm = allX[0:x_sm,:,] Y_sm = ally[0:x_sm] Y_sm=to_categorical(Y_sm, 2) print (X.shape, Y.shape, allX.shape, ally.shape, X_sm.shape) ################################### # Image transformations ################################### # normalisation of images img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Create extra synthetic training data by flipping & rotating images img_aug = ImageAugmentation() img_aug.add_random_flip_leftright() img_aug.add_random_rotation(max_angle=25.) ################################### # Define network architecture ################################### # Input is a 27x33 image with 3 color channels (red, green and blue) network = input_data(shape=[None, 27, 33, 3]) #,data_preprocessing=img_prep, #data_augmentation=img_aug) # 1: Convolution layer with 32 filters, each 3x3x3 conv_1 = conv_2d(network, 32, 3, activation='relu', name='conv_1') # 2: Max pooling layer network = max_pool_2d(conv_1, 2) # 3: Convolution layer with 64 filters conv_2 = conv_2d(network, 64, 3, activation='relu', name='conv_2') #4: Convolution layer with 64 filters conv_3 = conv_2d(conv_2, 64, 3, activation='relu', name='conv_3') # 5: Max pooling layer network = max_pool_2d(conv_3, 2) # 6: Fully-connected 512 node layer network = fully_connected(network, 1024, activation='relu') # 7: Dropout layer to combat overfitting network = dropout(network, 0.5) # 8: Fully-connected layer with two outputs network = fully_connected(network, 2, activation='softmax') # Configure how the network will be trained acc = Accuracy(name="Accuracy") network = regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001, metric=acc) # Wrap the network in a model object model = tflearn.DNN(network, checkpoint_path='model_glasses_6.tflearn', max_checkpoints = 3, tensorboard_verbose = 3, tensorboard_dir='tmp/tflearn_logs/') ################################### # Train model for 1000 epochs ################################### model.fit(X_sm, Y_sm, validation_set=(X_validation, Y_validation), batch_size=50, n_epoch=1000, run_id='model_glasses_6', show_metric=True) model.save('model_glasses_6_final.tflearn') # Evaluate model score = model.evaluate(X_test, Y_test) print('Test accuarcy: %0.4f%%' % (score[0] * 100))
def main(_): dirname = os.path.join(FLAGS.buckets, "") (X, Y), (X_test, Y_test) = load_data(dirname) print("load data done") X, Y = shuffle(X, Y) Y = to_categorical(Y, 10) Y_test = to_categorical(Y_test, 10) # Real-time data preprocessing img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Real-time data augmentation img_aug = ImageAugmentation() img_aug.add_random_flip_leftright() img_aug.add_random_rotation(max_angle=25.) # Convolutional network building network = input_data(shape=[None, 32, 32, 3], data_preprocessing=img_prep, data_augmentation=img_aug) network = conv_2d(network, 32, 3, activation='relu') network = max_pool_2d(network, 2) network = conv_2d(network, 64, 3, activation='relu') network = conv_2d(network, 64, 3, activation='relu') network = max_pool_2d(network, 2) network = fully_connected(network, 512, activation='relu') network = dropout(network, 0.5) network = fully_connected(network, 10, activation='softmax') network = regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001) # Train using classifier model = tflearn.DNN(network, tensorboard_verbose=0) # model.fit(X, Y, n_epoch=100, shuffle=True, validation_set=(X_test, Y_test), # show_metric=True, batch_size=96, run_id='cifar10_cnn') model_path = os.path.join(FLAGS.checkpointDir, "model.tfl") print(model_path) model.load(model_path) # predict_pic = os.path.join(FLAGS.buckets, "bird_mount_bluebird.jpg") # file_paths = tf.train.match_filenames_once(predict_pic) # input_file_queue = tf.train.string_input_producer(file_paths) # reader = tf.WholeFileReader() # file_path, raw_data = reader.read(input_file_queue) # img = tf.image.decode_jpeg(raw_data, 3) # img = tf.image.resize_images(img, [32, 32]) # prediction = model.predict([img]) # print (prediction[0]) predict_pic = os.path.join(FLAGS.buckets, "bird_bullocks_oriole.jpg") img_obj = file_io.read_file_to_string(predict_pic) file_io.write_string_to_file("bird_bullocks_oriole.jpg", img_obj) img = scipy.ndimage.imread("bird_bullocks_oriole.jpg", mode="RGB") # Scale it to 32x32 img = scipy.misc.imresize(img, (32, 32), interp="bicubic").astype(np.float32, casting='unsafe') # Predict prediction = model.predict([img]) print (prediction[0]) print (prediction[0]) #print (prediction[0].index(max(prediction[0]))) num=['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck'] print ("This is a %s"%(num[prediction[0].index(max(prediction[0]))]))
def _model5(): global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() def block35(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 32, 3, bias=False, activation=None,name='Conv2d_0b_3x3'))) tower_conv2_0 = relu(batch_normalization(conv_2d(net, 32, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 48,3, bias=False, activation=None, name='Conv2d_0b_3x3'))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 64,3, bias=False, activation=None, name='Conv2d_0c_3x3'))) tower_mixed = merge([tower_conv, tower_conv1_1, tower_conv2_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net def block17(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv_1_0 = relu(batch_normalization(conv_2d(net, 128, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv_1_1 = relu(batch_normalization(conv_2d(tower_conv_1_0, 160,[1,7], bias=False, activation=None,name='Conv2d_0b_1x7'))) tower_conv_1_2 = relu(batch_normalization(conv_2d(tower_conv_1_1, 192, [7,1], bias=False, activation=None,name='Conv2d_0c_7x1'))) tower_mixed = merge([tower_conv,tower_conv_1_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net def block8(net, scale=1.0, activation="relu"): tower_conv = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 192, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 224, [1,3], bias=False, activation=None, name='Conv2d_0b_1x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 256, [3,1], bias=False, name='Conv2d_0c_3x1'))) tower_mixed = merge([tower_conv,tower_conv1_2], mode='concat', axis=3) tower_out = relu(batch_normalization(conv_2d(tower_mixed, net.get_shape()[3], 1, bias=False, activation=None, name='Conv2d_1x1'))) net += scale * tower_out if activation: if isinstance(activation, str): net = activations.get(activation)(net) elif hasattr(activation, '__call__'): net = activation(net) else: raise ValueError("Invalid Activation.") return net num_classes = len(Y[0]) dropout_keep_prob = 0.8 network = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) conv1a_3_3 = relu(batch_normalization(conv_2d(network, 32, 3, strides=2, bias=False, padding='VALID',activation=None,name='Conv2d_1a_3x3'))) conv2a_3_3 = relu(batch_normalization(conv_2d(conv1a_3_3, 32, 3, bias=False, padding='VALID',activation=None, name='Conv2d_2a_3x3'))) conv2b_3_3 = relu(batch_normalization(conv_2d(conv2a_3_3, 64, 3, bias=False, activation=None, name='Conv2d_2b_3x3'))) maxpool3a_3_3 = max_pool_2d(conv2b_3_3, 3, strides=2, padding='VALID', name='MaxPool_3a_3x3') conv3b_1_1 = relu(batch_normalization(conv_2d(maxpool3a_3_3, 80, 1, bias=False, padding='VALID',activation=None, name='Conv2d_3b_1x1'))) conv4a_3_3 = relu(batch_normalization(conv_2d(conv3b_1_1, 192, 3, bias=False, padding='VALID',activation=None, name='Conv2d_4a_3x3'))) maxpool5a_3_3 = max_pool_2d(conv4a_3_3, 3, strides=2, padding='VALID', name='MaxPool_5a_3x3') tower_conv = relu(batch_normalization(conv_2d(maxpool5a_3_3, 96, 1, bias=False, activation=None, name='Conv2d_5b_b0_1x1'))) tower_conv1_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 48, 1, bias=False, activation=None, name='Conv2d_5b_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 64, 5, bias=False, activation=None, name='Conv2d_5b_b1_0b_5x5'))) tower_conv2_0 = relu(batch_normalization(conv_2d(maxpool5a_3_3, 64, 1, bias=False, activation=None, name='Conv2d_5b_b2_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2_0, 96, 3, bias=False, activation=None, name='Conv2d_5b_b2_0b_3x3'))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 96, 3, bias=False, activation=None,name='Conv2d_5b_b2_0c_3x3'))) tower_pool3_0 = avg_pool_2d(maxpool5a_3_3, 3, strides=1, padding='same', name='AvgPool_5b_b3_0a_3x3') tower_conv3_1 = relu(batch_normalization(conv_2d(tower_pool3_0, 64, 1, bias=False, activation=None,name='Conv2d_5b_b3_0b_1x1'))) tower_5b_out = merge([tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1], mode='concat', axis=3) net = repeat(tower_5b_out, 10, block35, scale=0.17) ''' tower_conv = relu(batch_normalization(conv_2d(net, 384, 3, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 3, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3'))) tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID',name='MaxPool_1a_3x3') net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3) net = repeat(net, 20, block17, scale=0.1) tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 3, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,3, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3'))) tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,3, bias=False, name='Conv2d_0b_3x3',activation=None))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 3, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3'))) tower_pool = max_pool_2d(net, 3, strides=2, padding='VALID', name='MaxPool_1a_3x3') ''' tower_conv = relu(batch_normalization(conv_2d(net, 384, 1, bias=False, strides=2,activation=None, padding='VALID', name='Conv2d_6a_b0_0a_3x3'))) tower_conv1_0 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1_0, 256, 1, bias=False, activation=None, name='Conv2d_6a_b1_0b_3x3'))) tower_conv1_2 = relu(batch_normalization(conv_2d(tower_conv1_1, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_6a_b1_0c_3x3'))) tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID',name='MaxPool_1a_3x3') net = merge([tower_conv, tower_conv1_2, tower_pool], mode='concat', axis=3) net = repeat(net, 20, block17, scale=0.1) tower_conv = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, activation=None, name='Conv2d_0a_1x1'))) tower_conv0_1 = relu(batch_normalization(conv_2d(tower_conv, 384, 1, bias=False, strides=2, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1 = relu(batch_normalization(conv_2d(net, 256, 1, bias=False, padding='VALID', activation=None,name='Conv2d_0a_1x1'))) tower_conv1_1 = relu(batch_normalization(conv_2d(tower_conv1,288,1, bias=False, strides=2, padding='VALID',activation=None, name='COnv2d_1a_3x3'))) tower_conv2 = relu(batch_normalization(conv_2d(net, 256,1, bias=False, activation=None,name='Conv2d_0a_1x1'))) tower_conv2_1 = relu(batch_normalization(conv_2d(tower_conv2, 288,1, bias=False, name='Conv2d_0b_3x3',activation=None))) tower_conv2_2 = relu(batch_normalization(conv_2d(tower_conv2_1, 320, 1, bias=False, strides=2, padding='VALID',activation=None, name='Conv2d_1a_3x3'))) tower_pool = max_pool_2d(net, 1, strides=2, padding='VALID', name='MaxPool_1a_3x3') #### net = merge([tower_conv0_1, tower_conv1_1,tower_conv2_2, tower_pool], mode='concat', axis=3) net = repeat(net, 9, block8, scale=0.2) net = block8(net, activation=None) net = relu(batch_normalization(conv_2d(net, 1536, 1, bias=False, activation=None, name='Conv2d_7b_1x1'))) net = avg_pool_2d(net, net.get_shape().as_list()[1:3],strides=2, padding='VALID', name='AvgPool_1a_8x8') net = flatten(net) net = dropout(net, dropout_keep_prob) loss = fully_connected(net, num_classes,activation='softmax') network = tflearn.regression(loss, optimizer='RMSprop', loss='categorical_crossentropy', learning_rate=0.0001) model = tflearn.DNN(network, checkpoint_path='inception_resnet_v2', max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir="./tflearn_logs/") model.fit(X, Y, n_epoch=epochNum, validation_set=(xTest, yTest), shuffle=True, show_metric=True, batch_size=batchNum, snapshot_step=2000, snapshot_epoch=False, run_id='inception_resnet_v2_oxflowers17') if modelStore: model.save(_id + '-model.tflearn')
def _model4(): # Taken from TFLearn examples and based on Googles Inception. DO NOT RUN!!!! global yTest, img_aug tf.reset_default_graph() img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() network = input_data(shape=[None, inputSize, inputSize, dim], name='input', data_preprocessing=img_prep, data_augmentation=img_aug) conv1_7_7 = conv_2d(network, 64, 7, strides=2, activation='relu', name='conv1_7_7_s2') pool1_3_3 = max_pool_2d(conv1_7_7, 3, strides=2) pool1_3_3 = local_response_normalization(pool1_3_3) conv2_3_3_reduce = conv_2d(pool1_3_3, 64, 1, activation='relu', name='conv2_3_3_reduce') conv2_3_3 = conv_2d(conv2_3_3_reduce, 192, 3, activation='relu', name='conv2_3_3') conv2_3_3 = local_response_normalization(conv2_3_3) pool2_3_3 = max_pool_2d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2') # 3a inception_3a_1_1 = conv_2d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1') inception_3a_3_3_reduce = conv_2d(pool2_3_3, 96, 1, activation='relu', name='inception_3a_3_3_reduce') inception_3a_3_3 = conv_2d(inception_3a_3_3_reduce, 128, filter_size=3, activation='relu', name='inception_3a_3_3') inception_3a_5_5_reduce = conv_2d(pool2_3_3, 16, filter_size=1, activation='relu', name='inception_3a_5_5_reduce') inception_3a_5_5 = conv_2d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name='inception_3a_5_5') inception_3a_pool = max_pool_2d(pool2_3_3, kernel_size=3, strides=1, name='inception_3a_pool') inception_3a_pool_1_1 = conv_2d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1') inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=3) # 3b inception_3b_1_1 = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_1_1') inception_3b_3_3_reduce = conv_2d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce') inception_3b_3_3 = conv_2d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu', name='inception_3b_3_3') inception_3b_5_5_reduce = conv_2d(inception_3a_output, 32, filter_size=1, activation='relu', name='inception_3b_5_5_reduce') inception_3b_5_5 = conv_2d(inception_3b_5_5_reduce, 96, filter_size=5, name='inception_3b_5_5') inception_3b_pool = max_pool_2d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool') inception_3b_pool_1_1 = conv_2d(inception_3b_pool, 64, filter_size=1, activation='relu', name='inception_3b_pool_1_1') inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat', axis=3, name='inception_3b_output') pool3_3_3 = max_pool_2d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3') # 4a inception_4a_1_1 = conv_2d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4a_3_3_reduce = conv_2d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce') inception_4a_3_3 = conv_2d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3') inception_4a_5_5_reduce = conv_2d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce') inception_4a_5_5 = conv_2d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5') inception_4a_pool = max_pool_2d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool') inception_4a_pool_1_1 = conv_2d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1') inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=3, name='inception_4a_output') # 4b inception_4b_1_1 = conv_2d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1') inception_4b_3_3_reduce = conv_2d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce') inception_4b_3_3 = conv_2d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3') inception_4b_5_5_reduce = conv_2d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce') inception_4b_5_5 = conv_2d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5') inception_4b_pool = max_pool_2d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool') inception_4b_pool_1_1 = conv_2d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1') inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=3, name='inception_4b_output') # 4c inception_4c_1_1 = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_1_1') inception_4c_3_3_reduce = conv_2d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce') inception_4c_3_3 = conv_2d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3') inception_4c_5_5_reduce = conv_2d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce') inception_4c_5_5 = conv_2d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5') inception_4c_pool = max_pool_2d(inception_4b_output, kernel_size=3, strides=1) inception_4c_pool_1_1 = conv_2d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1') inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=3, name='inception_4c_output') # 4d inception_4d_1_1 = conv_2d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1') inception_4d_3_3_reduce = conv_2d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce') inception_4d_3_3 = conv_2d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3') inception_4d_5_5_reduce = conv_2d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce') inception_4d_5_5 = conv_2d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5') inception_4d_pool = max_pool_2d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool') inception_4d_pool_1_1 = conv_2d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1') inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=3, name='inception_4d_output') # 4e inception_4e_1_1 = conv_2d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1') inception_4e_3_3_reduce = conv_2d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce') inception_4e_3_3 = conv_2d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3') inception_4e_5_5_reduce = conv_2d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce') inception_4e_5_5 = conv_2d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5') inception_4e_pool = max_pool_2d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool') inception_4e_pool_1_1 = conv_2d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1') inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5, inception_4e_pool_1_1], axis=3, mode='concat') pool4_3_3 = max_pool_2d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3') # 5a inception_5a_1_1 = conv_2d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1') inception_5a_3_3_reduce = conv_2d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce') inception_5a_3_3 = conv_2d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3') inception_5a_5_5_reduce = conv_2d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce') inception_5a_5_5 = conv_2d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5') inception_5a_pool = max_pool_2d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool') inception_5a_pool_1_1 = conv_2d(inception_5a_pool, 128, filter_size=1, activation='relu', name='inception_5a_pool_1_1') inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=3, mode='concat') # 5b inception_5b_1_1 = conv_2d(inception_5a_output, 384, filter_size=1, activation='relu', name='inception_5b_1_1') inception_5b_3_3_reduce = conv_2d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce') inception_5b_3_3 = conv_2d(inception_5b_3_3_reduce, 384, filter_size=3, activation='relu', name='inception_5b_3_3') inception_5b_5_5_reduce = conv_2d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce') inception_5b_5_5 = conv_2d(inception_5b_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5b_5_5') inception_5b_pool = max_pool_2d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool') inception_5b_pool_1_1 = conv_2d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1') inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=3, mode='concat') pool5_7_7 = avg_pool_2d(inception_5b_output, kernel_size=7, strides=1) pool5_7_7 = dropout(pool5_7_7, 0.4) # fc loss = fully_connected(pool5_7_7, len(yTest[0]), activation='softmax') network = regression(loss, optimizer='momentum', loss='categorical_crossentropy', learning_rate=0.001) # to train model = tflearn.DNN(network, checkpoint_path='model_googlenet', max_checkpoints=1, tensorboard_verbose=2) model.load(_path) pred = model.predict(xTest) df = pd.DataFrame(pred) df.to_csv(_path + ".csv") newList = pred.copy() newList = convert2(newList) if _CSV: makeCSV(newList) pred = convert2(pred) pred = convert3(pred) yTest = convert3(yTest) print(metrics.confusion_matrix(yTest, pred)) print(metrics.classification_report(yTest, pred)) print('Accuracy', accuracy_score(yTest, pred)) print() if _wrFile: writeTest(pred)
from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.conv import conv_2d, max_pool_2d from tflearn.layers.estimator import regression from tflearn.data_preprocessing import ImagePreprocessing from tflearn.data_augmentation import ImageAugmentation # Data loading and pre processing from tflearn.datasets import cifar10 (X,Y), (X_test, Y_test) = cifar10.load_data() X, Y = shuffle(X,Y) Y = to_categorical(Y, 10) Y_test = to_categorical(Y_test, 10) # Data preprocessing img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Data augmentation img_aug = ImageAugmentation() img_aug.add_random_flip_leftright() img_aug.add_random_rotation() # Building the CNN network = input_data(shape=[None, 32, 32, 3], data_preprocessing=img_prep, data_augmentation=img_aug, name='first_layer') network = max_pool_2d(network, 2) # Max pooling layer network = conv_2d(network, 64, 3 , activation='relu') network = conv_2d(network, 64, 3 , activation='relu') # Multiple convolution layers network = max_pool_2d(network, 2) # Max pooling layer network = fully_connected(network, 512, activation='relu')
def generate_image_preprocessing(self): # Real-time data preprocessing img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() return img_prep