I wrote very simple cnn code with spectrogram images, but
the accuracy is only 0.3~0.4
What do i have to add the other option to improve accuracy?
model.add(Conv2D(32, (3, 3), input_shape=X_train.shape[1:], padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64))
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(14))
model.add(Activation('softmax'))```
With the information you provide, there is zero chance to help you with the problem. The definition of your model looks correct (but you missed an activation function after the first dense layer if this is by accident). So here are some considerations:
Do you train long enough? Your model is quite big and therefore needs a long time to converge AND a large dataset to train with.
Is your dataset large enough and contains enough variance? When your dataset doesn't represent your problem well, you can't train.
Take a look at the Loss curves of your validation AND training set. Are you overfitting/underfitting?
Do you correctly normalize and preprocess your dataset? Try to transform the values of the images to a range of -1 to 1 or 0 to 1 with a float datatype.
Is your dataset balanced? As you are softmaxing 14 classes, you need a balanced dataset in order to train every single class.
Hoped this helped a little, if you need further help please provide some detailed descriptions of your problem and what are you doing in your whole process.
Related
:)
I have a Datset of ~16,000 .wav recording from 70 bird species.
I'm training a model using tensorflow to classify the mel-spectrogram of these recordings using Convolution based architectures.
One of the architectures used is simple multi-layer convolutional described below.
The pre-processing phase include:
extract mel-spectrograms and convert to dB Scale
segment audio to 1-second segment (pad with zero Or gaussian noise if residual is longer than 250ms, discard otherwise)
z-score normalization of training data - reduce mean and divide result by std
pre-processing while inference:
same as described above
z-score normalization BY training data - reduce mean (of training) and divide result by std (of training data)
I understand that the output layer's probabilities with sigmoid activation is not suppose to accumulate to 1, But I get many (8-10) very high prediction (~0.999) probabilities. and some is exactly 0.5.
The current test set correct classification rate is ~84%, tested with 10-fold cross validation, So it seems that the the network mostly operates well.
notes:
1.I understand there are similar features in the vocalization of different birds species, but the recieved probabilities doesn't seem to reflect them correctly
2. probabilities for example - a recording of natural noise:
Natural noise: 0.999
Mallard - 0.981
I'm trying to understand the reason for these results, if it's related the the data etc extensive mislabeling (probably not) or from another source.
Any help will be much appreciated! :)
EDIT: I use sigmoid because the probabilities of all classes are necessary, and I don't need them to accumulate to 1.
def convnet1(input_shape, numClasses, activation='softmax'):
# Define the network
model = tf.keras.Sequential()
model.add(InputLayer(input_shape=input_shape))
# model.add(Augmentations1(p=0.5, freq_type='mel', max_aug=2))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Conv2D(128, (5, 5), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (5, 5), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(Flatten())
# model.add(Dense(numClasses, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(numClasses, activation='sigmoid'))
model.compile(
loss='categorical_crossentropy',
metrics=['accuracy'],
optimizer=optimizers.Adam(learning_rate=0.001),
run_eagerly=False) # this parameter allows to debug and use regular functions inside layers: print(), save() etc..
return model
For future searches - this problem was solved, and the reason was found(!).
The initial batch size that was used was 256 or 512. reducing the batch size to 16 or 32 SOLVED THE PROBLEM, and now the difference in probabilities are as expected for training AND test set samples - very high for the correct label and very low for other classes.
I am building a model for autoencoder. I have a dataset of images(256x256) in LAB color space.
But i dont know, what is the right maximum compression point. I found example, when i have 176 x 176 x 1 (~30976), then the point is 22 x 22 x 512 (~247808).
But how is that calculated?
My model:
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(256, 256, 1)))
model.add(MaxPooling2D((2,2)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D((2,2)))
model.add(Conv2D(128, (3,3), activation='relu', padding='same'))
model.add(MaxPooling2D((2,2)))
model.add(Conv2D(256, (3,3), activation='relu', padding='same'))
model.add(MaxPooling2D((2,2)))
model.add(Conv2D(512, (3,3), activation='relu', padding='same'))
#Decoder
model.add(Conv2D(256, (3,3), activation='relu', padding='same'))
model.add(UpSampling2D((2, 2)))
model.add(Conv2D(128, (3,3), activation='relu', padding='same'))
model.add(UpSampling2D((2, 2)))
model.add(Conv2D(64, (3,3), activation='relu', padding='same'))
model.add(UpSampling2D((2, 2)))
model.add(Conv2D(2, (3, 3), activation='tanh', padding='same'))
model.add(UpSampling2D((2, 2)))
model.compile(optimizer='adam', loss='mse' , metrics=['accuracy'])
model.summary()
Figuring out these aspects of a network is more art than mathematics. As such, we cannot define a constant compression point without properly analyzing the data, which is the reason why neural nets are used in the first place.
We can however intuitively consider what happens at every layer. For example, in an image colorization problem, it is better not to use too many pooling layers since that discards a huge amount of information. A max pooling layer of size 2x2 with a stride of 2 discards 75% of its input data. This is much more useful in classification to eliminate improbable classes. Similarly, ReLU discards all negative data, and may not be the best function choice for the problem at hand.
Here are a few tips that may help with your specific problem:
Reduce the number of pooling layers. Before pooling, try increasing the number of trainable layers so that the model (intuitively) learns to aggregate important information to avoid pooling it out.
Change the activation to elu, LeakyReLU or such, that do not eliminate negative values, especially since the output requires negative values as well.
Maybe try BiLinear or BiCubic upsampling to maintain structure? I'd also suggest taking a look at the so called "magic" kernel here. Personally, I've had good results with it, though it takes time to implement it efficiently.
If you have enough GPU space, increase the number of channels. This particular point does not have much to consider, except overfitting in some cases.
Preferably, use a Conv2D layer as the final layer to compensate for artifacts while upsampling.
Keep in mind that these points are for general use cases. Models in research papers are a different case, and are not as simple as your architecture. All these points may or may not apply to a specific paper.
Let's say I have binary classification task and I'm using a CNN. Simply visualizing the CNN isn't very helpful as the input isn't images. However, I would like to know which particular filters contribute the most for an input sample to be considered a particular class.
Given the following architecture (implemented using Keras), how do I achieve this?
model = Sequential()
model.add(Conv2D(32, kernel_size=(10, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (10, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
I explored resources A and B. But neither seem helpful for what I want to do. If there are other suggestions for understanding what a network learns for non-image datasets, that'd be really helpful.
I am training a Convolutional Neural Network to recognize MRZ(Machine Readable Zone) characters, on a smartphone. I want to know if in order to improve accuracy I should train it with multiple fonts, even if MRZ only uses OCR-B. Also, the model does not perform on device with the same level of accuracy as in the python code I use to train/test it. Any ideas?
This is the architecture I'm using:
model = Sequential()
model.add(Convolution2D(filters=32, kernel_size=(3, 3), strides=(2, 2), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Convolution2D(filters=64, kernel_size=(1, 1), strides=(1, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
If MRZ use only one font, then you should use only this font to train your CNN.
In order to improve results, you should preprocess the image before passing it to the CNN, for example, at first identify text zones in an image and then pass them through CNN.
The accuracy of the model can change from a device to another because of processing unit architecture, for example, CPU and GPU will get different results due to numerical stability.
I'm trying to build a CNN to play a game online. This game to be precise:
https://www.gameeapp.com/game-bot/ibBTDViUP
I've collected images and labels for each image. These labels tell the network to press SPACE (output 1) or do nothing (output 0).
I'm training the network using Keras, like this:
history = model.fit_generator(
train_generator,
steps_per_epoch=2000 // batch_size,
epochs=3,
validation_data=validation_generator,
validation_steps=800 // batch_size)
The network looks like this:
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(275, 208, 1)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
The thing is. Most of the time the network ends up always outputting 1 or always zero even when the images are completely unrelated to the game images.
Am I modelling this problem the right way?
How can I make the best way for the network to be able to Identify to "not do" anything.
Please let me know if the question isn't clear and Thanks in advance!
You want to do binary image classification ( binary : is - is not ) and i think your net looks good. In Binary Image Classification with CNN - best practices for choosing "negative" dataset? are general hints for training binary image classification networks. In https://medium.com/#kylepob61392/airplane-image-classification-using-a-keras-cnn-22be506fdb53 is a complete guide for setting up image classification network in keras. i am not sure about the training possibly use plain model.fit() like in https://medium.com/#kylepob61392/airplane-image-classification-using-a-keras-cnn-22be506fdb53