I have 4 classes and building a Keras model for image classification problem. I have tried a couple of adjustments but accuracy is not going beyond 75% and still loss is 64%.
I have 90,400 images as a training set and 20,000 images for testing.
Here is my model.
model = Sequential()
model.add(Conv2D(32, kernel_size = (3, 3),input_shape=(100,100,3),activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(4, activation = 'softmax'))
model.compile(loss = 'sparse_categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
batch_size = 64
train_datagen = ImageDataGenerator (rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
test_datagen=ImageDataGenerator(rescale=1./255)
training_set = train_datagen.flow_from_directory('/dir/training_set', target_size=(100,100),batch_size=batch_size,class_mode='binary')
test_set = test_datagen.flow_from_directory('/dir/test_set',target_size=(100,100), batch_size=batch_size, class_mode='binary')
# 90,400 images I have under the training_set directory and 20,000 under the test directory.
model.fit(training_set, steps_per_epoch=90400//batch_size, epochs=1,validation_data=test_set, validation_steps= 20000//batch_size)
I tried adjusting layers and dropouts but no luck. Any ideas?
If I encounter something like this, I would do following:
Split my data into training-validation and test. Improve model by validation and use test to see final result.
Removing Dropout layers since I don't have a proof that model is overfitted.
If model is underfitted (your case),
3.a. Try different / bigger architecture and searching better hyperparameters
3.b. Training longer and try different optimization algorithms
If model is overfitted,
4.a. Try to get more data
4.b. Regularization (L2, dropout etc.)
4.c. Data augmentation
4.d. Searching better hyperparameters
Note: You can always consider transfer learning. Basically, transfer leaning is using gained information from a successful model for your model.
Consider
Adding multiple convolutional layers (with Max pooling in between) enables the model to learn "low level" and "higher level" features
Adding more epochs to enable the model to learn from the input pictures. Neural Networks only learn "a little bit" along the gradient each time, it often takes multiple up to many epochs to have a sufficiently trained model.
Maybe start with less pictures but increase the epochs (and add a second conv/max pool pair) to keep calculation time under control!
You could try using one of the existing models in Keras and train it from scratch.
I have used MobileNetV2 in the past and have gotten very good results.
When you initialize the model you can load pre-trained weights or None, and start traning from scratch with your images.
I was able to achieve accuracy with transfer learning using the pre-trained MobileNet model.
Attaching my code and confusion metrix here so it may be helpful to someone.
import pandas as pd
import numpy as np
import os
import keras
import matplotlib.pyplot as plt
from keras.layers import Dense,GlobalAveragePooling2D
from keras.applications import MobileNet
from keras.preprocessing import image
from keras.applications.mobilenet import preprocess_input
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.optimizers import Adam
base_model=MobileNet(weights='imagenet',include_top=False) #imports the mobilenet model and discards the last 1000 neuron layer.
x=base_model.output
x=GlobalAveragePooling2D()(x)
x=Dense(1024,activation='relu')(x) #we add dense layers so that the model can learn more complex functions and classify for better results.
x=Dense(1024,activation='relu')(x) #dense layer 2
x=Dense(512,activation='relu')(x) #dense layer 3
preds=Dense(3,activation='softmax')(x) #final layer with softmax activation
model=Model(inputs=base_model.input,outputs=preds)
for layer in model.layers[:20]:
layer.trainable=False
for layer in model.layers[20:]:
layer.trainable=True
train_data_path = '../train_dataset_path'
train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input, validation_split=0.2) #included in our dependencies
train_generator=train_datagen.flow_from_directory(train_data_path,
target_size=(224,224),
color_mode='rgb',
batch_size=32,
class_mode='categorical',
shuffle=True,
subset='training')
test_generator=train_datagen.flow_from_directory(train_data_path,
target_size=(224,224),
color_mode='rgb',
batch_size=32,
class_mode='categorical',
shuffle=False,
subset='validation')
model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])
step_size_train = train_generator.n//train_generator.batch_size
step_size_test = test_generator.n//test_generator.batch_size
model_history = model.fit(train_generator,
steps_per_epoch=step_size_train,
epochs=5,
validation_data=test_generator,
validation_steps=step_size_test)
model.save('tl_interior_model_2')
#Load the model
model = keras.models.load_model('tl_interior_model_2')
Related
I have trained a text classification model that works well. I wanted to get deeper and understand what words/phrases from a sentence were most impactful in the classification outcome. I want to understand what words are most important for each classification outcome
I am using Keras for the classification and below is the code I am using to train the model. It's a simple embedding plus max-pooling text classification model that I am using.
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping
# early stopping
callbacks = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', min_delta=0,
patience=5, verbose=2, mode='auto', restore_best_weights=True)
# select optimizer
opt = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999,
epsilon=1e-07, amsgrad=False, name="Adam")
embedding_dim = 50
# declare model
model = Sequential()
model.add(layers.Embedding(input_dim=vocab_size,
output_dim=embedding_dim,
input_length=maxlen))
model.add(layers.GlobalMaxPool1D())
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer=opt,
loss='binary_crossentropy',
metrics=['accuracy'])
model.summary()
# fit model
history = model.fit(X_tr, y_tr,
epochs=20,
verbose=True,
validation_data=(X_te, y_te),
batch_size=10, callbacks=[callbacks])
loss, accuracy = model.evaluate(X_tr, y_tr, verbose=False)
How do I extract the phrases/words that have the maximum impact on the classification outcome?
It seems that the keyword you need are "neural network interpretability" and "feature attribution". One of the best known methods in this area is called Integrated Gradients; it shows how model prediction depend on each input feature (each word embedding, in your case).
This tutorial shows how to implement IG in pure tensorflow for images, and this one uses the alibi library to highlight the words in the input text with the highest impact on a classification model.
I'm trying to do transfer learning on MobileNetV3-Small using Tensorflow 2.5.0 to predict dog breeds (133 classes) and since it got reasonable accuracy on the ImageNet dataset (1000 classes) I thought it should have no problem adapting to my problem.
I've tried a multitude of training variations and recently had a breakthrough but now my training stagnates at about 60% validation accuracy with minor fluctuations in validation loss (accuracy and loss curves for training and validation below).
I tried using ReduceLROnPlateau in the 3rd graph below, but it didn't help to improve matters. Can anyone suggest how I could improve the training?
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.layers import GlobalMaxPooling2D, Dense, Dropout, BatchNormalization
from tensorflow.keras.applications import MobileNetV3Large, MobileNetV3Small
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True # needed for working with this dataset
# define generators
train_datagen = ImageDataGenerator(vertical_flip=True, horizontal_flip=True,
rescale=1.0/255, brightness_range=[0.5, 1.5],
zoom_range=[0.5, 1.5], rotation_range=90)
test_datagen = ImageDataGenerator(rescale=1.0/255)
train_gen = train_datagen.flow_from_directory(train_dir, target_size=(224,224),
batch_size=32, class_mode="categorical")
val_gen = test_datagen.flow_from_directory(val_dir, target_size=(224,224),
batch_size=32, class_mode="categorical")
test_gen = test_datagen.flow_from_directory(test_dir, target_size=(224,224),
batch_size=32, class_mode="categorical")
pretrained_model = MobileNetV3Small(input_shape=(224,224,3), classes=133,
weights="imagenet", pooling=None, include_top=False)
# set all layers trainable because when I froze most of the layers the model didn't learn so well
for layer in pretrained_model.layers:
layer.trainable = True
last_output = pretrained_model.layers[-1].output
x = GlobalMaxPooling2D()(last_output)
x = BatchNormalization()(x)
x = Dense(512, activation='relu')(x)
x = Dense(133, activation='softmax')(x)
model = Model(pretrained_model.input, x)
model.compile(optimizer=Adam(learning_rate=1e-5), loss='categorical_crossentropy', metrics=['accuracy'])
# val_acc with min_delta 0.003; val_loss with min_delta 0.01
plateau = ReduceLROnPlateau(monitor="val_loss", mode="min", patience=5,
min_lr=1e-8, factor=0.3, min_delta=0.01,
verbose=1)
checkpointer = ModelCheckpoint(filepath=savepath, verbose=1, save_best_only=True,
monitor="val_accuracy", mode="max",
save_weights_only=True)
Your code looks good, but it seems to have one issue - you might be rescaling the inputs twice. According to the docs for MobilenetV3:
The preprocessing logic has been included in the mobilenet_v3 model implementation. Users are no longer required (...) to normalize the input data.
Now, in your code, there is:
test_datagen = ImageDataGenerator(rescale=1.0/255)
which essentially, makes the first model layers to rescale, already rescaled values.
The same applies for train_datagen.
You could try removing the rescale argument from both train and test loaders, or setting rescale=None.
This could also explain why the model did not learn well with the backbone frozen.
I am new here but 3 days of sleepless nights have been enough to drive me desperately to ask for help here :( To keep things short, I have been working on training a dog breed classification model, specifically this one from kaggle.
I had a full complete old code from 2 years ago that achieved decent accuracies around 70-80%. The code is as below:
# -*- coding: utf-8 -*-
#import the necessary libraries/modules
import numpy as np
import pandas as pd
import sys
from tensorflow.keras.applications import InceptionResNetV2, ResNet50
from keras.applications.inception_resnet_v2 import preprocess_input
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras.models import Model, Sequential
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras import backend as K
from keras import optimizers
from keras import models
from tensorflow.keras import layers
import tensorflow as tf
from sklearn.model_selection import train_test_split
train_df = pd.read_csv("./data/train_labels.csv", dtype=str)
validation_df = pd.read_csv("./data/validation_labels.csv", dtype=str)
#returns the number of unique classes we are dealing with
num_classes = train_df["breed"].nunique()
#image_size to be used
dimensions = 400
image_size = (dimensions, dimensions)
#rescale and preprocess image
train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input, rescale=1./255, horizontal_flip=True, vertical_flip=True, zoom_range=0.1, width_shift_range=0.05, height_shift_range=0.05)
validation_datagen=ImageDataGenerator(rescale=1./255)
print("ImageDataGenerator Done.")
#train_generator for training data
train_generator=train_datagen.flow_from_dataframe(
dataframe=train_df,
directory="./data/train/images/",
x_col="id",
y_col="breed",
batch_size=16,
seed=42,
shuffle=True,
class_mode="categorical",
target_size=image_size)
#validation_generator for validation data
validation_generator=validation_datagen.flow_from_dataframe(
dataframe=validation_df,
directory="./data/train/images/",
x_col="id",
y_col="breed",
batch_size=16,
seed=42,
shuffle=True,
class_mode="categorical",
target_size=image_size)
print("Generators done.")
#using inceptionresnetv2 as base model
base_model = InceptionResNetV2(weights='imagenet', include_top=False, input_shape=(dimensions, dimensions, 3))
#set last 160 layers of base_model to be trainable
for layer in base_model.layers:
layer.trainable=False
#create new model, add base_model and add final layers
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(num_classes, activation='softmax'))
#show summary of model
print(model.summary())
#compile the model
model.compile(optimizer=optimizers.RMSprop(0.01), loss='categorical_crossentropy', metrics=['accuracy'])
#save model after every epoch
checkpoint = ModelCheckpoint('./models/model-{epoch:02d}-{val_accuracy:.2f}.hdf5', monitor='val_accuracy', mode='max', save_best_only=True, save_weights_only=False, verbose=1, save_freq="epoch")
callbacks_list = [checkpoint]
STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size
STEP_SIZE_VALID=validation_generator.n//validation_generator.batch_size
hist = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=validation_generator,
validation_steps=STEP_SIZE_VALID,
epochs=30, callbacks=callbacks_list, verbose=1)
However, I have stayed away from machine learning for a while now and only recently decided to give it a try again (so I just ran this code again for a start). To my surprise, running the same piece of code yielded accuracies of 1% and the loss was exploding as you can see below:
Low Accuracy and Exploding Loss
Checking for deprecation issues yielded nothing and so I have decided to try running another code instead from this tutorial.
Apart from the file paths, my code is exactly the same as in the tutorial and yet, the accuracies and losses are behaving in exactly the same manner. I have checked and the images are fine, detected and processed. I have ran this on google colab, on a macbook and on an ubuntu VPS and still the accuracies and losses behave the same. At this point I am at a complete loss as to what could be the reason for this observation. I understand the guide is 2 years old as well so I am inclined to think there are certain functions that are no longer applicable or being misused but I have checked line by line again and again to no fruition ;-;
If there is any kind soul out there that could advice on why I am experiencing this I would really greatly appreciate it ;-; Thank you in advance!
I am working on an image classification problem with keras and tensorflow. I am using the VGG16 model with Imagenet weights and I am importing my data using the ImageDataGenerator from Keras.
Now I've been reading that one should always rescale the images using 1./255 for an efficient tranining. However, once I implement the scaling my model performs significantly worse than before. Changing the learning rate and batch size didn't help either.
Now I am questioning whether this is possible or if my model has some error. I am using standard .jpg image files.
from keras.preprocessing.image import ImageDataGenerator
IMAGE_SIZE = 224
BATCH_SIZE = 32
num_classes = 27
main_path = "C:/Users/abc/data"
final_path = os.path.join(main_path, "ML_DATA")
labels = listdir(gesamt_path)
data_generator = ImageDataGenerator(rescale=1./255, ### rescaling done here
validation_split=0.20)
train_generator = data_generator.flow_from_directory(final_path, target_size=(IMAGE_SIZE, IMAGE_SIZE), shuffle=True, seed=13,
class_mode='categorical', batch_size=BATCH_SIZE, subset="training")
validation_generator = data_generator.flow_from_directory(final_path, target_size=(IMAGE_SIZE, IMAGE_SIZE), shuffle=False, seed=13,
class_mode='categorical', batch_size=BATCH_SIZE, subset="validation")
Model definition and training
vgg16_model = keras.applications.vgg16.VGG16(weights='imagenet', include_top=True)
model = Sequential()
for layer in vgg16_model.layers[:-1]:
model.add(layer)
for layer in model.layers:
layer.trainable = False
model.add(Dense(num_classes, activation='softmax'))
model.compile(Adam(lr=.001), loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit_generator(train_generator,
validation_data=validation_generator,
epochs=85, verbose=1,callbacks=[tbCallBack,earlystopCallback])
It could be that Imagenet Weights are not compatible with your new image dimension.
I see that your only trainable layer is the very last layer, a dense layer, which doesn’t know anything about image dimension. My suggestion is to also let the first few convolutional layers to be trainable, so that those layers can adapt to the rescaling.
Working with ResNet and imagenet weights I improved my results using:
ImageDataGenerator(preprocessing_function=preprocess_input)
With rescaling I obtained worse results too.
This information was useful to me:
https://github.com/matterport/Mask_RCNN/issues/231
I was wondering if it was possible to save a partly trained Keras model and continue the training after loading the model again.
The reason for this is that I will have more training data in the future and I do not want to retrain the whole model again.
The functions which I am using are:
#Partly train model
model.fit(first_training, first_classes, batch_size=32, nb_epoch=20)
#Save partly trained model
model.save('partly_trained.h5')
#Load partly trained model
from keras.models import load_model
model = load_model('partly_trained.h5')
#Continue training
model.fit(second_training, second_classes, batch_size=32, nb_epoch=20)
Edit 1: added fully working example
With the first dataset after 10 epochs the loss of the last epoch will be 0.0748 and the accuracy 0.9863.
After saving, deleting and reloading the model the loss and accuracy of the model trained on the second dataset will be 0.1711 and 0.9504 respectively.
Is this caused by the new training data or by a completely re-trained model?
"""
Model by: http://machinelearningmastery.com/
"""
# load (downloaded if needed) the MNIST dataset
import numpy
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from keras.models import load_model
numpy.random.seed(7)
def baseline_model():
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, init='normal', activation='relu'))
model.add(Dense(num_classes, init='normal', activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
if __name__ == '__main__':
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# flatten 28*28 images to a 784 vector for each image
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')
# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
# build the model
model = baseline_model()
#Partly train model
dataset1_x = X_train[:3000]
dataset1_y = y_train[:3000]
model.fit(dataset1_x, dataset1_y, nb_epoch=10, batch_size=200, verbose=2)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
#Save partly trained model
model.save('partly_trained.h5')
del model
#Reload model
model = load_model('partly_trained.h5')
#Continue training
dataset2_x = X_train[3000:]
dataset2_y = y_train[3000:]
model.fit(dataset2_x, dataset2_y, nb_epoch=10, batch_size=200, verbose=2)
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
Edit 2: tensorflow.keras remarks
For tensorflow.keras change the parameter nb_epochs to epochs in the model fit. The imports and basemodel function are:
import numpy
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import load_model
numpy.random.seed(7)
def baseline_model():
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
Actually - model.save saves all information need for restarting training in your case. The only thing which could be spoiled by reloading model is your optimizer state. To check that - try to save and reload model and train it on training data.
Most of the above answers covered important points. If you are using recent Tensorflow (TF2.1 or above), Then the following example will help you. The model part of the code is from Tensorflow website.
import tensorflow as tf
from tensorflow import keras
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
def create_model():
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])
return model
# Create a basic model instance
model=create_model()
model.fit(x_train, y_train, epochs = 10, validation_data = (x_test,y_test),verbose=1)
Please save the model in *.tf format. From my experience, if you have any custom_loss defined, *.h5 format will not save optimizer status and hence will not serve your purpose if you want to retrain the model from where we left.
# saving the model in tensorflow format
model.save('./MyModel_tf',save_format='tf')
# loading the saved model
loaded_model = tf.keras.models.load_model('./MyModel_tf')
# retraining the model
loaded_model.fit(x_train, y_train, epochs = 10, validation_data = (x_test,y_test),verbose=1)
This approach will restart the training where we left before saving the model. As mentioned by others, if you want to save weights of best model or you want to save weights of model every epoch you need to use keras callbacks function (ModelCheckpoint) with options such as save_weights_only=True, save_freq='epoch', and save_best_only.
For more details, please check here and another example here.
The problem might be that you use a different optimizer - or different arguments to your optimizer. I just had the same issue with a custom pretrained model, using
reduce_lr = ReduceLROnPlateau(monitor='loss', factor=lr_reduction_factor,
patience=patience, min_lr=min_lr, verbose=1)
for the pretrained model, whereby the original learning rate starts at 0.0003 and during pre-training it is reduced to the min_learning rate, which is 0.000003
I just copied that line over to the script which uses the pre-trained model and got really bad accuracies. Until I noticed that the last learning rate of the pretrained model was the min learning rate, i.e. 0.000003. And if I start with that learning rate, I get exactly the same accuracies to start with as the output of the pretrained model - which makes sense, as starting with a learning rate that is 100 times bigger than the last learning rate used in the pretrained model will result in a huge overshoot of GD and hence in heavily decreased accuracies.
Notice that Keras sometimes has issues with loaded models, as in here.
This might explain cases in which you don't start from the same trained accuracy.
You might also be hitting Concept Drift, see Should you retrain a model when new observations are available. There's also the concept of catastrophic forgetting which a bunch of academic papers discuss. Here's one with MNIST Empirical investigation of catastrophic forgetting
All above helps, you must resume from same learning rate() as the LR when the model and weights were saved. Set it directly on the optimizer.
Note that improvement from there is not guaranteed, because the model may have reached the local minimum, which may be global. There is no point to resume a model in order to search for another local minimum, unless you intent to increase the learning rate in a controlled fashion and nudge the model into a possibly better minimum not far away.
If you are using TF2, use the new saved_model method(format pb). More information available here and here.
model.fit(x=X_train, y=y_train, epochs=10,callbacks=[model_callback])#your first training
tf.saved_model.save(model, save_to_dir_path) #save the model
del model #to delete the model
model = tf.keras.models.load_model(save_to_dir_path)
model.fit(x=X_train, y=y_train, epochs=10,callbacks=[model_callback])#your second training
It is completely okay to train a model with a saved model. I trained the saved model with the same data and found it was giving good accuracy. Moreover, the time taken was quite less in each epoch.
Here is the code have a look:
from keras.models import load_model
model = load_model('/content/drive/MyDrive/CustomResNet/saved_models/model_1.h5')
history=model.fit(train_gen,validation_data=valid_gen,epochs=5)