I am training my model with a dataset of 200 images. I have created a binary classification CNN that looks like this one:
classifier = Sequential()
# Adding a first convolutional layer
classifier.add(Convolution2D(48, 3, input_shape = (320, 320, 3), activation = 'relu'))
classifier.add(MaxPooling2D())
# Adding a second convolutional layer
classifier.add(Convolution2D(48, 3, activation = 'relu'))
classifier.add(MaxPooling2D())
# Adding a third convolutional layer
classifier.add(Convolution2D(48, 3, activation = 'relu'))
classifier.add(MaxPooling2D())
#Flattening
classifier.add(Flatten())
#Full connected
classifier.add(Dense(256, activation = 'relu'))
#Full connected
classifier.add(Dense(256, activation = 'sigmoid'))
#Dropout
classifier.add(Dropout(0.5))
#Full connected
classifier.add(Dense(1, activation = 'sigmoid'))
# Compiling the CNN
opt = keras.optimizers.Adam(learning_rate=0.001)
classifier.compile(optimizer = opt, loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.summary()
I am also using Image Data Augmentation and Early Stopping based on val_accuracy with a patience of 10.
My results are the following:
Result graph validation accuracy
The best validation accuracy I get is 0.9231 at the 21st epoch. Should I stop the training with a custom callback once I surpass 92% or is it a bad practice?
Would it be a good practice to set a custom callback that stops training
The best practice here is to save the model every time the validation accuracy hits a maximum, but to keep training. Alternatively, you could save a model after each epoch, and choose the best one to use by checking the validation graph (I'd suggest epoch 11 here. After 11 the validation graph is just oscillating, which is mostly noise).
Finally, 200 images is rarely enough to get good results. You want thousands or tens of thousands at least. Even your validation set should have at least 100 images so that even minor changes to the model show smooth changes in the validation curve. You should also consider adding some data augmentation if you aren't doing it already.
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 have 4 classes, each with 1350 images. The validation set has 20% of the total images (it is generated automatically). The training model uses MobilenetV2 network:
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights='imagenet')
The model is created:
model = tf.keras.Sequential([
base_model,
tf.keras.layers.Conv2D(32, 3, activation='relu', kernel_regularizer=regularizers.l2(0.001)),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.MaxPool2D(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(4, activation='softmax', kernel_regularizer=regularizers.l2(0.001))
])
The model is trained through 20 epochs and then fine tunning is done in 15 epochs. The result is as follows:
Image of the model trained before fine tunning
Image of the model trained after 15 epochs and fine tunning
A bit difficult to tell without the numeric values of validation loss but I would say the results before fine tuning are slightly over fitting and for after fine tuning less over fitting. A couple of additional things you could do. One is try using an adjustable learning rate using the callback tf.keras.callbacks.ReduceLROnPlateau. Set it up to monitor validation loss. Documentation is here. I set factor=.5 and patience=1. Second replace the Flatten layer with tf.keras.layers.GlobalMaxPool2D and see if it improves the validation loss.
New to the field of deep learning and currently working on this competition for predicting the earthquake damage to buildings.
The model I created starts at an accuracy of .56 but remains at this for any number of epochs i let it run. When finished, the model only predicts one of the three classes (which I one hot encoded into a dataframe with three columns). Changing the number of layers, optimizers, data preparation, dropout wont change anything. Even trying to overfit my model with the over-parameterization of the neural network will still have the same accuracy and a non-learning model.
What am I doing wrong?
This is my code:
model = keras.models.Sequential()
model.add(keras.layers.Dense(64, input_dim = 85, activation = "relu"))
keras.layers.Dropout(0.3)
model.add(keras.layers.Dense(128, activation = "relu"))
keras.layers.Dropout(0.3)
model.add(keras.layers.Dense(256, activation = "relu"))
keras.layers.Dropout(0.3)
model.add(keras.layers.Dense(512, activation = "relu"))
model.add(keras.layers.Dense(3, activation = "softmax"))
adam = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(optimizer = adam,
loss='categorical_crossentropy',
metrics = ['accuracy'])
history = model.fit(traindata, trainlabels,
epochs = 5,
validation_split = 0.2,
verbose = 1,)
There's nothing visually wrong with your model, but it may be too haevy to learn any useful features.
Try normalizing your input with https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
Start with only 2 layers, and a few numbers of neurons.
Increase batch_size and try learning_rate scheduling.
Observe the validation_accuracy, stop when it starts to overfit.
Finally, for a 3-class classification, 56% accuracy is better than baseline, remmeber it's a competition so the data is not dummy playground data which you can expect to get a 90% accuracy with an MLP in the first try.
Finally, try hyperparameter optimization with tuner.
I'm trying to use a CNN-LSTM network with Keras in order to analyze videos. I read about it and run into TimeDistributed function and some examples.
Actually, I tried the network described below, which is in fact composed by a convolutional and pooling layers followed by recurrent and dense layers.
model = Sequential()
model.add(TimeDistributed(Conv2D(2, (2,2), activation= 'relu' ), input_shape=(None, IMG_SIZE, IMG_SIZE, 3)))
model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(50))
model.add(Dense(50, activation = 'softmax'))
model.compile(loss = 'categorical_crossentropy' , optimizer = 'adam' , metrics = ['acc'])
I haven't tested properly the model, since my dataset is too small. However, during training process the network reaches accuracy 0.98 in 4-5 epochs (perhaps it is overfitting, but it isn't a problem yet because I hope to get a suitable dataset later).
Then, I read about how to use a pretrained convolutional network (MobileNet, ResNet or Inception) as a feature extractor for LSTM network, such that I use the following code:
inputs = Input(shape = (frames, IMG_SIZE, IMG_SIZE, 3))
cnn_base = InceptionV3(include_top = False, weights='imagenet', input_shape = (IMG_SIZE, IMG_SIZE, 3))
cnn_out = GlobalAveragePooling2D()(cnn_base.output)
cnn = Model(inputs=cnn_base.input, outputs=cnn_out)
encoded_frames = TimeDistributed(cnn)(inputs)
encoded_sequence = LSTM(256)(encoded_frames)
hidden_layer = Dense(1024, activation="relu")(encoded_sequence)
outputs = Dense(50, activation="softmax")(hidden_layer)
model = Model([inputs], outputs)
In this case, when training the model it always shows accuracy ~0.02 (it is the baseline 1/50).
Since the first model at least learned anything, I am wondering if there is any error with the way the network is build in the second case.
Has anybody faced this situation? Any advice?
Thank you.
The reason is you have very small amount of data and retraining the complete Inception V3 weights. Either you have to train the model with more amount of data OR train the model with more number of epochs with hyper parameter tuning. You can find more about hyper parameter training here.
The ideal way is to freeze the base model by base_model.trainable = False and just train the new layers that you have added on top of the Inception V3 layers.
OR
Unfreeze the top layers of the base model(Inception V3 layers) and set the bottom layers to be un-trainable. You can do it as below -
# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))
# Fine-tune from this layer onwards
fine_tune_at = 100
# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at]:
layer.trainable = False
I am running a CNN for left and right shoeprint classfication. I have 190,000 training images and I use 10% of it for validation. My model is setup as shown below. I get the paths of all the images, read them in and resize them. I normalize the image, and then fit it to the model. My issue is that I have stuck at a training accuracy of 62.5% and a loss of around 0.6615-0.6619. Is there something wrong that I am doing? How can I stop this from happening?
Just some interesting points to note:
I first tested this on 10 images I was having the same issue but changing the optimizer to adam and batch size to 4 worked.
I then tested on more and more images, but each time I would need to change the batch size to get improvements in the accuracy and loss. With 10,000 images I had to use a batch size of 500 and optimizer rmsprop. However, the accuracy and loss only really began to change after epoch 10.
I am now training on 190,000 images and I cannot increase the batch size as my GPU is at is max.
imageWidth = 50
imageHeight = 150
def get_filepaths(directory):
file_paths = []
for filename in files:
filepath = os.path.join(root, filename)
file_paths.append(filepath) # Add it to the list.
return file_paths
def cleanUpPaths(fullFilePaths):
cleanPaths = []
for f in fullFilePaths:
if f.endswith(".png"):
cleanPaths.append(f)
return cleanPaths
def getTrainData(paths):
trainData = []
for i in xrange(1,190000,2):
im = image.imread(paths[i])
im = image.imresize(im, (150,50))
im = (im-255)/float(255)
trainData.append(im)
trainData = np.asarray(trainData)
right = np.zeros(47500)
left = np.ones(47500)
trainLabels = np.concatenate((left, right))
trainLabels = np_utils.to_categorical(trainLabels)
return (trainData, trainLabels)
#create the convnet
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(imageWidth,imageHeight,1),strides=1))#32
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu',strides=1))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(1, 3)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (1, 2), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
sgd = SGD(lr=0.01)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop',metrics=['accuracy'])
#prepare the training data*/
trainPaths = get_filepaths("better1/train")
trainPaths = cleanUpPaths(trainPaths)
(trainData, trainLabels) = getTrainData(trainPaths)
trainData = np.reshape(trainData,(95000,imageWidth,imageHeight,1)).astype('float32')
trainData = (trainData-255)/float(255)
#train the convnet***
model.fit(trainData, trainLabels, batch_size=500, epochs=50, validation_split=0.2)
#/save the model and weights*/
model.save('myConvnet_model5.h5');
model.save_weights('myConvnet_weights5.h5');
I've had this issue a number of times now, so thought to make a little recap of it and possible solutions etc. to help people in the future.
Issue: Model predicts one of the 2 (or more) possible classes for all data it sees*
Confirming issue is occurring: Method 1: accuracy for model stays around 0.5 while training (or 1/n where n is number of classes). Method 2: Get the counts of each class in predictions and confirm it's predicting all one class.
Fixes/Checks (in somewhat of an order):
Double Check Model Architecture: use model.summary(), inspect the model.
Check Data Labels: make sure the labelling of your train data hasn't got mixed up somewhere in the preprocessing etc. (it happens!)
Check Train Data Feeding Is Randomised: make sure you are not feeding your train data to the model one class at a time. For instance if using ImageDataGenerator().flow_from_directory(PATH), check that param shuffle=True and that batch_size is greater than 1.
Check Pre-Trained Layers Are Not Trainable:** If using a pre-trained model, ensure that any layers that use pre-trained weights are NOT initially trainable. For the first epochs, only the newly added (randomly initialised) layers should be trainable; for layer in pretrained_model.layers: layer.trainable = False should be somewhere in your code.
Ramp Down Learning Rate: Keep reducing your learning rate by factors of 10 and retrying. Note you will have to fully reinitialize the layers you are trying to train each time you try a new learning rate. (For instance, I had this issue that was only solved once I got down to lr=1e-6, so keep going!)
If any of you know of more fixes/checks that could possible get the model training properly then please do contribute and I'll try to update the list.
**Note that is common to make more of the pretrained model trainable, once the new layers have been initially trained "enough"
*Other names for the issue to help searches get here...
keras tensorflow theano CNN convolutional neural network bad training stuck fixed not static broken bug bugged jammed training optimization optimisation only 0.5 accuracy does not change only predicts one single class wont train model stuck on class model resetting itself between epochs keras CNN same output
You can try to add a BatchNornmalization() layer after MaxPooling2D(). It works for me.
I just have 2 things more to add to the great list of DBCerigo.
Check activation functions: some layers have linear activation function by default, if you do not insert some non linearity into your model it wont be able to generalize, so the net will try to learn how to separate linearly a feature space that is not linear. Making sure you have your non linearity set is a good checkpoint.
Check Model Complexity: if you have a relatively simple model and it learns only till the 1st or the 2nd epoch and then it stalls, it may be that it is trying to learn something too complex. Try making the model deeper. This usually happens when working with frozen models with only 1 or 2 layers unfrozen.
Although the 2nd one may be obvious, I run into his problem once and I lost lots of time checking everythin (data, batches, LR...) before figuring out.
Hope this helps
I would try a couple of things. A lower learning rate should help with more data. Generally, adapting the optimizer should help. Additionally your network seems really small, you might want to increase the capacity of the model by adding layers or increasing the number of filters in the layers.
A better description on how to apply deep learning in practice is given here.
in my case it is the activification function matters. I change from 'sgd' to 'a'