Tensorflow fine-tunning AlexNet
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包括Tensorflow下的 AlexNet 网络模型搭建、测试、使用自己的数据做 fine-tunning、批量测试训练结果
主要参考文章《Finetuning AlexNet with TensorFlow》，文章《TensorFlow微调AlexNet》是对其的翻译
tensorflow 比 caffe 要快很多。。。
AlexNet网络搭建
每个卷积和池化层后添加了输出尺寸的计算，方便fine-tunning时对于不同尺寸（227*227）的处理
与Caffe需要指明 pad 值不同，Tensorflow通过 ‘VALID’ 和 ‘SAME’ 区分两种 padding 方法
- VALID
边界按情况填0，输出尺寸out_h=ceil(in_h−filter_h+1strides_h) - SAME
不填0，输出尺寸out_h=ceil(in_hstrides_h)
def create(self): # 1st Layer: Conv (w ReLu) -> Pool -> Lrn conv1 = conv(self.X, 11, 11, 96, 4, 4, padding = 'VALID', name = 'conv1') pool1 = max_pool(conv1, 3, 3, 2, 2, padding = 'VALID', name = 'pool1') norm1 = lrn(pool1, 2, 2e-05, 0.75, name = 'norm1' # calc output size out_h = int(math.ceil(float(self.IN_SIZE[0]-11+1)/4)) out_w = int(math.ceil(float(self.IN_SIZE[1]-11+1)/4)) out_h = int(math.ceil(float(out_h-3+1)/2)) out_w = int(math.ceil(float(out_w-3+1)/2)) # 2nd Layer: Conv (w ReLu) -> Pool -> Lrn with 2 groups conv2 = conv(norm1, 5, 5, 256, 1, 1, groups = 2, name = 'conv2') pool2 = max_pool(conv2, 3, 3, 2, 2, padding = 'VALID', name ='pool2') norm2 = lrn(pool2, 2, 2e-05, 0.75, name = 'norm2') # calc output size out_h = int(math.ceil(float(out_h-3+1)/2)) out_w = int(math.ceil(float(out_w-3+1)/2)) # 3rd Layer: Conv (w ReLu) conv3 = conv(norm2, 3, 3, 384, 1, 1, name = 'conv3') # 4th Layer: Conv (w ReLu) splitted into two groups conv4 = conv(conv3, 3, 3, 384, 1, 1, groups = 2, name = 'conv4') # 5th Layer: Conv (w ReLu) -> Pool splitted into two groups conv5 = conv(conv4, 3, 3, 256, 1, 1, groups = 2, name = 'conv5') pool5 = max_pool(conv5, 3, 3, 2, 2, padding = 'VALID', name = 'pool5') # calc output size out_h = int(math.ceil(float(out_h-3+1)/2)) out_w = int(math.ceil(float(out_w-3+1)/2)) # 6th Layer: Flatten -> FC (w ReLu) -> Dropout flattened = tf.reshape(pool5, [-1, out_h*out_w*256]) fc6 = fc(flattened, out_h*out_w*256, 4096, name='fc6') dropout6 = dropout(fc6, self.KEEP_PROB) # 7th Layer: FC (w ReLu) -> Dropout fc7 = fc(dropout6, 4096, 4096, name = 'fc7') dropout7 = dropout(fc7, self.KEEP_PROB) # 8th Layer: FC and return unscaled activations # (for tf.nn.softmax_cross_entropy_with_logits) self.fc8 = fc(dropout7, 4096, self.NUM_CLASSES, relu = False, name='fc8')辅助函数
def conv(x, filter_height, filter_width, num_filters, stride_y, stride_x, name, padding='SAME', groups=1): # Get number of input channels input_channels = int(x.get_shape()[-1]) # Create lambda function for the convolution convolve = lambda i, k: tf.nn.conv2d(i, k, strides = [1, stride_y, stride_x, 1], padding = padding) with tf.variable_scope(name) as scope: # Create tf variables for the weights and biases of the conv layer weights = tf.get_variable('weights', shape = [filter_height, filter_width, input_channels/groups, num_filters]) biases = tf.get_variable('biases', shape = [num_filters]) if groups == 1: conv = convolve(x, weights) # In the cases of multiple groups, split inputs & weights and else: # Split input and weights and convolve them separately input_groups = tf.split(axis = 3, num_or_size_splits=groups, value=x) weight_groups = tf.split(axis = 3, num_or_size_splits=groups, value=weights) output_groups = [convolve(i, k) for i,k in zip(input_groups, weight_groups)] # Concat the convolved output together again conv = tf.concat(axis = 3, values = output_groups) # Add biases bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape().as_list()) # Apply relu function relu = tf.nn.relu(bias, name = scope.name) return reludef fc(x, num_in, num_out, name, relu = True): with tf.variable_scope(name) as scope: # Create tf variables for the weights and biases weights = tf.get_variable('weights', shape=[num_in, num_out], trainable=True) biases = tf.get_variable('biases', [num_out], trainable=True) # Matrix multiply weights and inputs and add bias act = tf.nn.xw_plus_b(x, weights, biases, name=scope.name) if relu == True: # Apply ReLu non linearity relu = tf.nn.relu(act) return relu else: return actdef max_pool(x, filter_height, filter_width, stride_y, stride_x, name, padding='SAME'): return tf.nn.max_pool(x, ksize=[1, filter_height, filter_width, 1], strides = [1, stride_y, stride_x, 1], padding = padding, name = name)def lrn(x, radius, alpha, beta, name, bias=1.0): return tf.nn.local_response_normalization(x, depth_radius = radius, alpha = alpha, beta = beta, bias = bias, name = name)def dropout(x, keep_prob): return tf.nn.dropout(x, keep_prob)初始权重模型载入（.npy）
《caffe-tensorflow》提供了从 caffe 模型到 tensorflow 模型转换的工具
本文直接从这里下载.npy模型
def load_initial_weights(self, session): # Load the weights into memory weights_dict = np.load(self.WEIGHTS_PATH, encoding = 'bytes').item() # Loop over all layer names stored in the weights dict for op_name in weights_dict: # Check if the layer is one of the layers that should be reinitialized if op_name not in self.SKIP_LAYER: with tf.variable_scope(op_name, reuse = True): # Loop over list of weights/biases and assign them to their corresponding tf variable for data in weights_dict[op_name]: # Biases if len(data.shape) == 1: var = tf.get_variable('biases', trainable = False) session.run(var.assign(data)) # Weights else: var = tf.get_variable('weights', trainable = False) session.run(var.assign(data))AlexNet 网络测试
class alexnet_test(object): def __init__(self): self.PRE_MODEL = 'bvlc_alexnet.npy' def test_imagenet(self, imgs_): num_classes = 1000 skip_layer = [] imgs = [] #mean of imagenet dataset in BGR imagenet_mean = np.array([104., 117., 124.], dtype=np.float32) #plot images fig = plt.figure(figsize=(15,6)) for i, img_ in enumerate(imgs_): img = cv2.imread(img_) imgs.append(img) fig.add_subplot(1,3,i+1) plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) plt.axis('off') #placeholder for input and dropout rate x = tf.placeholder(tf.float32, [1, 227, 227, 3]) keep_prob = tf.placeholder(tf.float32) #create model with default config ( == no skip_layer and 1000 units in the last layer) model = alexnet(x, keep_prob, num_classes, skip_layer, weights_path=self.PRE_MODEL) #define activation of last layer as score score = model.fc8 #create op to calculate softmax softmax = tf.nn.softmax(score) with tf.Session() as sess: # Initialize all variables sess.run(tf.global_variables_initializer()) # Load the pretrained weights into the model model.load_initial_weights(sess) # Create figure handle fig2 = plt.figure(figsize=(15,6)) # Loop over all images for i, image in enumerate(imgs): # Convert image to float32 and resize to (227x227) img = cv2.resize(image.astype(np.float32), (227,227)) # Subtract the ImageNet mean img -= imagenet_mean # Reshape as needed to feed into model img = img.reshape((1,227,227,3)) # Run the session and calculate the class probability probs = sess.run(softmax, feed_dict={x: img, keep_prob: 1}) # Get the class name of the class with the highest probability class_name = class_names[np.argmax(probs)] # Plot image with class name and prob in the title fig2.add_subplot(1,3,i+1) plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) plt.title("Class: " + class_name + ", probability: %.4f" %probs[0,np.argmax(probs)]) plt.axis('off') plt.show()fine-tunning
def fine_tuning(self, train_list, test_list, mean, snapshot, filewriter_path): # Learning params learning_rate = 0.001 num_epochs = 80000 batch_size = 50 # Network params in_img_size = (332, 675) #(height, width) dropout_rate = 0.5 num_classes = 6 train_layers = ['fc6', 'fc7', 'fc8'] # How often we want to write the tf.summary data to disk display_step = 40 x = tf.placeholder(tf.float32, [batch_size, in_img_size[0], in_img_size[1], 3]) y = tf.placeholder(tf.float32, [None, num_classes]) keep_prob = tf.placeholder(tf.float32) # Initialize model model = alexnet(x, keep_prob, num_classes, train_layers, in_size=in_img_size #link variable to model output score = model.fc8 # List of trainable variables of the layers we want to train var_list = [v for v in tf.trainable_variables() if v.name.split('/')[0] in train_layers] # Op for calculating the loss with tf.name_scope("cross_ent"): loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = score, labels = y)) # Train op with tf.name_scope("train"): # Get gradients of all trainable variables gradients = tf.gradients(loss, var_list) gradients = list(zip(gradients, var_list)) # Create optimizer and apply gradient descent to the trainable variables optimizer = tf.train.GradientDescentOptimizer(learning_rate) train_op = optimizer.apply_gradients(grads_and_vars=gradients) # Add gradients to summary for gradient, var in gradients: tf.summary.histogram(var.name + '/gradient', gradient) # Add the variables we train to the summary for var in var_list: tf.summary.histogram(var.name, var) # Add the loss to summary tf.summary.scalar('cross_entropy', loss) # Evaluation op: Accuracy of the model with tf.name_scope("accuracy"): correct_pred = tf.equal(tf.argmax(score, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Add the accuracy to the summary tf.summary.scalar('accuracy', accuracy) # Merge all summaries together merged_summary = tf.summary.merge_all() # Initialize the FileWriter writer = tf.summary.FileWriter(filewriter_path) # Initialize an saver for store model checkpoints saver = tf.train.Saver() # Initalize the data generator seperately for the training and validation set train_generator = ImageDataGenerator(train_list, horizontal_flip = True, shuffle = False, mean=mean, scale_size=in_img_size, nb_classes=num_classes) val_generator = ImageDataGenerator(test_list, shuffle = False, mean=mean, scale_size=in_img_size, nb_classes=num_classes) # Get the number of training/validation steps per epoch train_batches_per_epoch = np.floor(train_generator.data_size / batch_size).astype(np.int16) val_batches_per_epoch = np.floor(val_generator.data_size / batch_size).astype(np.int16) # Start Tensorflow session with tf.Session() as sess: # Initialize all variables sess.run(tf.global_variables_initializer()) # Add the model graph to TensorBoard writer.add_graph(sess.graph) # Load the pretrained weights into the non-trainable layer model.load_initial_weights(sess) print("{} Start training...".format(datetime.now())) print("{} Open Tensorboard at --logdir {}".format(datetime.now(), filewriter_path)) # Loop over number of epochs for epoch in range(num_epochs): print("{} Epoch number: {}/{}".format(datetime.now(), epoch+1, num_epochs)) step = 1 while step < train_batches_per_epoch: # Get a batch of images and labels batch_xs, batch_ys = train_generator.next_batch(batch_size) # And run the training op sess.run(train_op, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout_rate}) # Generate summary with the current batch of data and write to file if step%display_step == 0: s = sess.run(merged_summary, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) writer.add_summary(s, epoch*train_batches_per_epoch + step) step += 1 # Validate the model on the entire validation set print("{} Start validation".format(datetime.now())) test_acc = 0. test_count = 0 for _ in range(val_batches_per_epoch): batch_tx, batch_ty = val_generator.next_batch(batch_size) acc = sess.run(accuracy, feed_dict={x: batch_tx, y: batch_ty, keep_prob: 1.}) test_acc += acc test_count += 1 test_acc /= test_count print("{} Validation Accuracy = {:.4f}".format(datetime.now(), test_acc)) # Reset the file pointer of the image data generator val_generator.reset_pointer() train_generator.reset_pointer() print("{} Saving checkpoint of model...".format(datetime.now())) #save checkpoint of the model if epoch % display_step == 0: checkpoint_name = os.path.join(snapshot, 'model_epoch'+str(epoch)+'.ckpt') save_path = saver.save(sess, checkpoint_name) print("{} Model checkpoint saved at {}".format(datetime.now(), checkpoint_name))另一个终端，tensorboard –logdir train_log，打开浏览器，127.0.0.1:6006 查看训练细节
batch predict
def predict_batch(self, val_list, mean, weight_file, result_file): in_img_size = (332, 675) #(height, width) dropout_rate = 0.5 num_classes = 6 train_layers = [] x = tf.placeholder(tf.float32, [1, in_img_size[0], in_img_size[1], 3]) model = alexnet(x, 1., num_classes, train_layers, in_size=in_img_size, weights_path=weight_file) score = model.fc8 softmax = tf.nn.softmax(score) val_generator = ImageDataGenerator(val_list, horizontal_flip = False, shuffle = False, mean=mean, scale_size=in_img_size, nb_classes=num_classes) precision = np.zeros((num_classes+1, num_classes), dtype=np.float) total_presion = 0. with tf.Session() as sess: sess.run(tf.global_variables_initializer()) tf.train.Saver().restore(sess, weight_file) self._start_end_time[0] = time.clock() for index in range(val_generator.data_size): print 'handing %d / %d ...\r'%(index+1, val_generator.data_size), img_ = val_generator.images[index] label = val_generator.labels[index] img = cv2.imread(img_) img = cv2.resize(img, (val_generator.scale_size[1], val_generator.scale_size[0])) img = img.reshape(1, val_generator.scale_size[0], val_generator.scale_size[1], 3) img = img.astype(np.float32) probs = sess.run(softmax, feed_dict={x: img}) guess = np.argmax(probs) if guess == label: precision[guess][guess] += 1 total_presion += 1 else: precision[guess][int(val_generator.labels[index])] += 1 self._start_end_time[1] = time.clock() for i in range(num_classes): for j in range(num_classes): precision[num_classes][i] += precision[j][i] for i in range(num_classes): for j in range(num_classes): precision[i][j] /= precision[num_classes][j] total_presion /= val_generator.data_size slaped = (self._start_end_time[1] - self._start_end_time[0]) / val_generator.data_size file = open(result_file, 'w') file.write('model: ' + weight_file + '\n') print '\n#####################################################################' file.writelines(['################################################################\n']) text_ = '' for i in range(num_classes): print ' %d'%i, text_ += ' %d'%i print '\n' file.write(text_ + '\n') for i in range(num_classes): print ' %d'%i, file.write(' ' + str(i)) for j in range(num_classes): str_preci = ' %.2f'%precision[i][j] print ' %.2f '%precision[i][j], file.write(str_preci) print '\n' file.write('\n') print '\ntotal precision: %.2f'%total_presion print 'average speed: %.4f / image'%slaped str_preci = 'total precision: %.2f'%total_presion file.writelines(['\n' + str_preci + '\n']) str_slaped = 'average speed: %.4f s / image'%slaped file.write(str_slaped + '\n') file.close()代码：https://github.com/yayo13/tensorflow_finetunning_alexnet
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