I have a model which I built on top of ResNet. I am using 25k Similar type of Images. My images have text as well as some diagram. When I used the Euclidean Distance + Binary loss, I got an accuracy of 95% with Inception but same with Triplet Hard/ Semi Hard Loss gave me nan loss and almost 0 accuracy. Please tell me if there is something wrong with the code structure.
import tensorflow_addons as tfa
from tensorflow.keras.applications.resnet50 import preprocess_input as res50_pre, ResNet50
shape = (224,224,3)
lr = 0.001
loss = tfa.losses.TripletSemiHardLoss()
epochs = 50
batch_size = 128 #254 gives 'log' referenced before assignment error
datagen = ImageDataGenerator(preprocessing_function=res50_pre,validation_split=0.2)
train_data = datagen.flow_from_dataframe(df,x_col='path',y_col='label',class_mode='sparse',target_size=(224,224),
batch_size=batch_size,subset='training',seed=SEED)
val_data = datagen.flow_from_dataframe(df,x_col='path',y_col='label',class_mode='sparse',target_size=(224,224),
batch_size=batch_size,subset='validation',seed=SEED)
base_model = ResNet50(weights='imagenet',input_shape=shape,include_top=False,pooling='avg')
base_model.trainable = True
inputs = keras.Input(shape=shape)
x = base_model(inputs,training=True)
outputs = keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))(x) # L2 normalize embeddings
model = keras.Model(inputs, outputs)
for layer in model.layers: # set all the parameters trainable
layer.trainable = True
model.compile(optimizer=tf.keras.optimizers.Adam(lr),loss=loss,metrics=['accuracy'])
history = model.fit(train_data,epochs=epochs,steps_per_epoch=len(train_data)//batch_size,validation_data=val_data,verbose=2)
My group has values like 1,2,3 [Not in order and some missing] which represent the same type of data. I used Sparse after converting the value to str(1), str(3) etc.
My DataFrame looks like this:
increase batch size to reduce probability of a mini batch not including any triplets.
Edit: I published a package for generating TF/Keras balanced batches to solve this problem https://github.com/ma7555/kerasgen
Related
I have a task to write a neural network. On input of 9 neurons, and output of 4 neurons for a multiclass classification problem. I have tried different models and for all of them:
Drop-out mechanism is used.
Batch normalization is used.
And the resulting neural networks all are overfitting. Precision is <80%, I want to have min 90% precision. Loss is 0.8 on the median.
Please, can you suggest to me what model I should use?
Dataset:
TMS_coefficients.RData file
Part of my code:
(trainX, testX, trainY, testY) = train_test_split(dataset,
values, test_size=0.25, random_state=42)
# модель нейронки
visible = layers.Input(shape=(9,))
hidden0 = layers.Dense(64, activation="tanh")(visible)
batch0 = layers.BatchNormalization()(hidden0)
drop0 = layers.Dropout(0.3)(batch0)
hidden1 = layers.Dense(32, activation="tanh")(drop0)
batch1 = layers.BatchNormalization()(hidden1)
drop1 = layers.Dropout(0.2)(batch1)
hidden2 = layers.Dense(128, activation="tanh")(drop1)
batch2 = layers.BatchNormalization()(hidden2)
drop2 = layers.Dropout(0.5)(batch2)
hidden3 = layers.Dense(64, activation="tanh")(drop2)
batch3 = layers.BatchNormalization()(hidden3)
output = layers.Dense(4, activation="softmax")(batch3)
model = tf.keras.Model(inputs=visible, outputs=output)
model.compile(optimizer=tf.keras.optimizers.Adam(0.0001),
loss='categorical_crossentropy',
metrics=['Precision'],)
history = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=5000, batch_size=256)
From the loss curve, I can say it is not overfitting at all! In fact, your model is underfitting. Why? because, when you have stopped training, the loss curve for the validation set has not become flat yet. That means, your model still has the potential to do well if it was trained more.
The model overfits when the training loss is decreasing (or remains the same) but the validation loss gradually increases without decreasing. This is clearly not the case
So, what you can do:
Try training longer.
Add more layers.
Try different activation functions like ReLU instead of tanh.
Use lower dropout (probably your model is struggling to learn for high value of dropouts).
Make sure you have shuffled your data before train-test splitting (if you are using sklearn for train_test_split() then it is done by default) and also check if the test data is similar to the train data and both of them goes under the same preprocessing steps.
I need to train a picture classification model with 15 categories. Because it needs to be deployed to OpenMV, the model size cannot exceed 1M, otherwise it cannot be loaded by openmv. I used the trained model mobilenetv2. As in the example on Keras's website, I placed the convolution basis of mobilenetv2 at the bottom of my model, and then added a softmax activated dense layer at the top. The training effect is good, and the accuracy has reached
90%, but the problem is derived The size of H5 model reaches 3M.
I tried to use a fool migration learning website. https://studio.edgeimpulse.com/
The classification model exported from the website is only 600KB(after INT8 quantized). What causes my model to be too large?
Here is the structure of my network:
def mobile_net_v2(data_augmentation, input_shape):
base_model = keras.applications.mobilenet_v2.MobileNetV2(
weights='imagenet',
input_shape=input_shape,
alpha=0.35,
include_top=False)
inputs = keras.Input(shape=input_shape)
base_model.trainable = False
x = data_augmentation(inputs)
x = layers.Rescaling(1. / 255)(x)
x = base_model(x)
x = layers.Flatten()(x)
outputs = layers.Dense(15, activation='softmax')(x)
return keras.Model(inputs, outputs)
The input size is (96, 96)
I would like to access the training point(s) at a training iteration and incorporate a soft constraint into my loss function by using data points not included in the training set. I will use this post as a reference.
import numpy as np
import keras.backend as K
from keras.layers import Dense, Input
from keras.models import Model
# Some random training data and labels
features = np.random.rand(100, 5)
labels = np.random.rand(100, 2)
# Simple neural net with three outputs
input_layer = Input((20,))
hidden_layer = Dense(16)(input_layer)
output_layer = Dense(3)(hidden_layer)
# Model
model = Model(inputs=input_layer, outputs=output_layer)
#each training point has another data pair. In the real example, I will have multiple
#supporters. That is why I am using dict.
holder = np.random.rand(100, 5)
iter = np.arange(start=1, stop=features.shape[0], step=1)
supporters = {}
for i,j in zip(iter, holder): #i represent the ith training data
supporters[i]=j
# Write a custom loss function
def custom_loss(y_true, y_pred):
# Normal MSE loss
mse = K.mean(K.square(y_true-y_pred), axis=-1)
new_constraint = ....
return(mse+new_constraint)
model.compile(loss=custom_loss, optimizer='sgd')
model.fit(features, labels, epochs=1, ,batch_size=1=1)
For simplicity, let us assume that I'd like to minimize the minimum absolute value difference between the prediction value and the prediction of the pair data stored in supporters by using the fixed network weights. Also, assume that I pass one training point at each batch. However, I could not figure out how to perform this opeartion. I've tried something shown below, but clearly, it is not correct.
new_constraint = K.sum(y_pred - model.fit(supporters))
Fit is the procedure of training evaluating the model. I think that it would be better for your problem to load a new instance of your model with your current weights and evaluate the batch loss in order to calculate the loss of the main model.
main_model = Model() # This is your main training model
def custom_loss_1(y_true, y_pred): # Avoid recursive calls
mse = K.mean(K.square(y_true-y_pred), axis=-1)
return mse
def custom_loss(y_true, y_pred):
support_model = tf.keras.models.clone_model(main_model) # You copy the main model but the weights are uninitialized
support_model.build((20,)) # You build with inputs same as your support data
support_model.compile(loss=custom_loss_1, optimizer='sgd')
support_model.set_weights(main_model.get_weights()) # You load the weight of the main model
mse = custom_loss_1(y_true, y_pred)
# You just want to evaluate the model, not to train. If you have more
# metrics than just loss the use support_model.evaluate(supporters)[0]
new_constraint = K.sum(y_pred - support_model.predict(supporters)) # predict to get the output, evaluate to get the metrics
return(mse+new_constraint)
I am facing a strange problem when adversarially training a resnet-50, and I am not sure whether is's a logical error, or a bug somewhere in the code/libraries.
I am adversarially training a resnet-50 thats loaded from Keras, using the FastGradientMethod from cleverhans, and expecting the adversarial accuracy to rise at least above 90% (probably 99.x%). The training algorithm, training- and attack-params should be visible in the code.
The problem, as already stated in the title is, that the accuracy is stuck at 5% after training ~3000 of 39002 training inputs in the first epoch. (GermanTrafficSignRecognitionBenchmark, GTSRB).
When training without and adversariy loss function, the accuracy does not get stuck after 3000 samples, but continues to rise > 0.95 in the first epoch.
When substituting the network with a lenet-5, alexnet and vgg19, the code works as expected, and an accuracy absolutely comparabele to the non-adversarial, categorical_corssentropy lossfunction is achieved. I've also tried running the procedure using solely tf-cpu and different versions of tensorflow, the result is always the same.
Code for obtaining ResNet-50:
def build_resnet50(num_classes, img_size):
from tensorflow.keras.applications import ResNet50
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense, Flatten
resnet = ResNet50(weights='imagenet', include_top=False, input_shape=img_size)
x = Flatten(input_shape=resnet.output.shape)(resnet.output)
x = Dense(1024, activation='sigmoid')(x)
predictions = Dense(num_classes, activation='softmax', name='pred')(x)
model = Model(inputs=[resnet.input], outputs=[predictions])
return model
Training:
def lr_schedule(epoch):
# decreasing learning rate depending on epoch
return 0.001 * (0.1 ** int(epoch / 10))
def train_model(model, xtrain, ytrain, xtest, ytest, lr=0.001, batch_size=32,
epochs=10, result_folder=""):
from cleverhans.attacks import FastGradientMethod
from cleverhans.utils_keras import KerasModelWrapper
import tensorflow as tf
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.callbacks import LearningRateScheduler, ModelCheckpoint
sgd = SGD(lr=lr, decay=1e-6, momentum=0.9, nesterov=True)
model(model.input)
wrap = KerasModelWrapper(model)
sess = tf.compat.v1.keras.backend.get_session()
fgsm = FastGradientMethod(wrap, sess=sess)
fgsm_params = {'eps': 0.01,
'clip_min': 0.,
'clip_max': 1.}
loss = get_adversarial_loss(model, fgsm, fgsm_params)
model.compile(loss=loss, optimizer=sgd, metrics=['accuracy'])
model.fit(xtrain, ytrain,
batch_size=batch_size,
validation_data=(xtest, ytest),
epochs=epochs,
callbacks=[LearningRateScheduler(lr_schedule)])
Loss-function:
def get_adversarial_loss(model, fgsm, fgsm_params):
def adv_loss(y, preds):
import tensorflow as tf
tf.keras.backend.set_learning_phase(False) #turn off dropout during input gradient calculation, to avoid unconnected gradients
# Cross-entropy on the legitimate examples
cross_ent = tf.keras.losses.categorical_crossentropy(y, preds)
# Generate adversarial examples
x_adv = fgsm.generate(model.input, **fgsm_params)
# Consider the attack to be constant
x_adv = tf.stop_gradient(x_adv)
# Cross-entropy on the adversarial examples
preds_adv = model(x_adv)
cross_ent_adv = tf.keras.losses.categorical_crossentropy(y, preds_adv)
tf.keras.backend.set_learning_phase(True) #turn back on
return 0.5 * cross_ent + 0.5 * cross_ent_adv
return adv_loss
Versions used:
tf+tf-gpu: 1.14.0
keras: 2.3.1
cleverhans: > 3.0.1 - latest version pulled from github
It is a side-effect of the way we estimate the moving averages on BatchNormalization.
The mean and variance of the training data that you used are different from the ones of the dataset used to train the ResNet50. Because the momentum on the BatchNormalization has a default value of 0.99, with only 10 iterations it does not converge quickly enough to the correct values for the moving mean and variance. This is not obvious during training when the learning_phase is 1 because BN uses the mean/variance of the batch. Nevertheless when we set learning_phase to 0, the incorrect mean/variance values which are learned during training significantly affect the accuracy.
You can fix this problem by below approachs:
More iterations
Reduce the size of the batch from 32 to 16(to perform more updates per epoch) and increase the number of epochs from 10 to 250. This way the moving average and variance will converge to the correct values.
Change the momentum of BatchNormalization
Keep the number of iterations fixed but change the momentum of the BatchNormalization layer to update more aggressively the rolling mean and variance (not recommended for production models).
On the original snippet, add the following code between reading the base_model and defining the new layers:
# ....
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=input_shape)
# PATCH MOMENTUM - START
import json
conf = json.loads(base_model.to_json())
for l in conf['config']['layers']:
if l['class_name'] == 'BatchNormalization':
l['config']['momentum'] = 0.5
m = Model.from_config(conf['config'])
for l in base_model.layers:
m.get_layer(l.name).set_weights(l.get_weights())
base_model = m
# PATCH MOMENTUM - END
x = base_model.output
# ....
Would also recommend you to try another hack provided bu us here.
UPDATED:
i'm building a Neural Network for my final project and i need some help with it.
I'm trying to build a rnn to do sentiment analysis over Spanish text. I have about 200,000 labeled tweets and i vectorized them using a word2vec with a Spanish embedding
Dataset & Vectorization:
I erased duplicates and split the dataset into training and testing sets.
Padding, unknown and end of sentence tokens are applied when vectorizing.
I mapped the #mentions to known names in the word2vec model. Example: #iamthebest => "John"
My model:
My data tensor has shape = (batch_size, 20, 300).
I have 3 classes: neutral, positive and negative, so my target tensor has shape = (batch_size, 3)
I use BasicLstm cells and dynamic rnn to build the net.
I use Adam Optimizer, and softmax_cross entropy for the loss calculation
I use a dropout wrapper to decrease the overfitting.
Last run:
I have tried with different configurations and non of them seem to work.
Last setup: 2 Layers, 512 batch size, 15 epochs and 0.001 of lr.
Weak points for me:
im worried about the final layer and the handing of the final state in the dynamic_rnn
Code:
# set variables
num_epochs = 15
tweet_size = 20
hidden_size = 200
vec_size = 300
batch_size = 512
number_of_layers= 1
number_of_classes= 3
learning_rate = 0.001
TRAIN_DIR="/checkpoints"
tf.reset_default_graph()
# Create a session
session = tf.Session()
# Inputs placeholders
tweets = tf.placeholder(tf.float32, [None, tweet_size, vec_size], "tweets")
labels = tf.placeholder(tf.float32, [None, number_of_classes], "labels")
# Placeholder for dropout
keep_prob = tf.placeholder(tf.float32)
# make the lstm cells, and wrap them in MultiRNNCell for multiple layers
def lstm_cell():
cell = tf.contrib.rnn.BasicLSTMCell(hidden_size)
return tf.contrib.rnn.DropoutWrapper(cell=cell, output_keep_prob=keep_prob)
multi_lstm_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(number_of_layers)], state_is_tuple=True)
# Creates a recurrent neural network
outputs, final_state = tf.nn.dynamic_rnn(multi_lstm_cells, tweets, dtype=tf.float32)
with tf.name_scope("final_layer"):
# weight and bias to shape the final layer
W = tf.get_variable("weight_matrix", [hidden_size, number_of_classes], tf.float32, tf.random_normal_initializer(stddev=1.0 / math.sqrt(hidden_size)))
b = tf.get_variable("bias", [number_of_classes], initializer=tf.constant_initializer(1.0))
sentiments = tf.matmul(final_state[-1][-1], W) + b
prob = tf.nn.softmax(sentiments)
tf.summary.histogram('softmax', prob)
with tf.name_scope("loss"):
# define cross entropy loss function
losses = tf.nn.softmax_cross_entropy_with_logits(logits=sentiments, labels=labels)
loss = tf.reduce_mean(losses)
tf.summary.scalar("loss", loss)
with tf.name_scope("accuracy"):
# round our actual probabilities to compute error
accuracy = tf.to_float(tf.equal(tf.argmax(prob,1), tf.argmax(labels,1)))
accuracy = tf.reduce_mean(tf.cast(accuracy, dtype=tf.float32))
tf.summary.scalar("accuracy", accuracy)
# define our optimizer to minimize the loss
with tf.name_scope("train"):
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)
#tensorboard summaries
merged_summary = tf.summary.merge_all()
logdir = "tensorboard/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + "/"
writer = tf.summary.FileWriter(logdir, session.graph)
# initialize any variables
tf.global_variables_initializer().run(session=session)
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
# load our data and separate it into tweets and labels
train_tweets = np.load('data_es/train_vec_tweets.npy')
train_labels = np.load('data_es/train_vec_labels.npy')
test_tweets = np.load('data_es/test_vec_tweets.npy')
test_labels = np.load('data_es/test_vec_labels.npy')
**HERE I HAVE THE LOOP FOR TRAINING AND TESTING, I KNOW ITS FINE**
I have already solved my problem. After reading some papers and more trial and error, I figured out what my mistakes were.
1) Dataset: I had a large dataset, but I didn't format it properly.
I checked the distribution of tweet labels (Neutral, Positive and Negative), realized there was a disparity in the distribution of said tweets and normalized it.
I cleaned it up even more by erasing url hashtags and unnecessary punctuation.
I shuffled prior to vectorization.
2) Initialization:
I initialized the MultiRNNCell with zeros and I changed my custom final layer to tf.contrib.fully_connected. I also added the initialization of the bias and weight matrix. (By fixing this, I started to see better loss and accuracy plots in Tensorboard)
3) Dropout:
I read this paper, Recurrent Dropout without Memory Loss, and I changed my dropouts accordingly; I started seeing improvements in the loss and accuracy.
4) Decaying the learning rate:
I added an exponential decaying rate after 10,000 steps to control over-fitting.
Final results:
After applying all of these changes, I achieved a test accuracy of 84%, which is acceptable because my data set still sucks.
My final network config was:
num_epochs = 20
tweet_size = 20
hidden_size = 400
vec_size = 300
batch_size = 512
number_of_layers= 2
number_of_classes= 3
start_learning_rate = 0.001