Keras accuracy does not change

The most likely reason is that the optimizer is not suited to your dataset. Here is a list of Keras optimizers from the documentation.

I recommend you first try SGD with default parameter values. If it still doesn't work, divide the learning rate by 10. Do that a few times if necessary. If your learning rate reaches 1e-6 and it still doesn't work, then you have another problem.

In summary, replace this line:

model.compile(loss = "categorical_crossentropy", optimizer = "adam")

with this:

from keras.optimizers import SGD
opt = SGD(lr=0.01)
model.compile(loss = "categorical_crossentropy", optimizer = opt)

and change the learning rate a few times if it doesn't work.

If it was the problem, you should see the loss getting lower after just a few epochs.

Another solution that I do not see mentioned here, but caused a similar problem for me was the activiation function of the last neuron, especialy if it is relu and not something non linear like sigmoid.

In other words, it might help you to use a non-linear activation function in the last layer

Last layer:

model.add(keras.layers.Dense(1, activation='relu'))

Output:

7996/7996 [==============================] - 1s 76us/sample - loss: 6.3474 - accuracy: 0.5860
Epoch 2/30
7996/7996 [==============================] - 0s 58us/sample - loss: 6.3473 - accuracy: 0.5860
Epoch 3/30
7996/7996 [==============================] - 0s 58us/sample - loss: 6.3473 - accuracy: 0.5860
Epoch 4/30
7996/7996 [==============================] - 0s 57us/sample - loss: 6.3473 - accuracy: 0.5860
Epoch 5/30
7996/7996 [==============================] - 0s 58us/sample - loss: 6.3473 - accuracy: 0.5860
Epoch 6/30
7996/7996 [==============================] - 0s 60us/sample - loss: 6.3473 - accuracy: 0.5860
Epoch 7/30
7996/7996 [==============================] - 0s 57us/sample - loss: 6.3473 - accuracy: 0.5860
Epoch 8/30
7996/7996 [==============================] - 0s 57us/sample - loss: 6.3473 - accuracy: 0.5860

Now I used a non linear activation function:

model.add(keras.layers.Dense(1, activation='sigmoid'))

Output:

7996/7996 [==============================] - 1s 74us/sample - loss: 0.7663 - accuracy: 0.5899
Epoch 2/30
7996/7996 [==============================] - 0s 59us/sample - loss: 0.6243 - accuracy: 0.5860
Epoch 3/30
7996/7996 [==============================] - 0s 56us/sample - loss: 0.5399 - accuracy: 0.7580
Epoch 4/30
7996/7996 [==============================] - 0s 56us/sample - loss: 0.4694 - accuracy: 0.7905
Epoch 5/30
7996/7996 [==============================] - 0s 57us/sample - loss: 0.4363 - accuracy: 0.8040
Epoch 6/30
7996/7996 [==============================] - 0s 60us/sample - loss: 0.4139 - accuracy: 0.8099
Epoch 7/30
7996/7996 [==============================] - 0s 58us/sample - loss: 0.3967 - accuracy: 0.8228
Epoch 8/30
7996/7996 [==============================] - 0s 61us/sample - loss: 0.3826 - accuracy: 0.8260

This is not directly a solution to the original answer, but as the answer is #1 on Google when searching for this problem, it might benefit someone.

If the accuracy is not changing, it means the optimizer has found a local minimum for the loss. This may be an undesirable minimum. One common local minimum is to always predict the class with the most number of data points. You should use weighting on the classes to avoid this minimum.

from sklearn.utils import compute_class_weight
classWeight = compute_class_weight('balanced', outputLabels, outputs) 
classWeight = dict(enumerate(classWeight))
model.fit(X_train, y_train, batch_size = batch_size, nb_epoch = nb_epochs, show_accuracy = True, verbose = 2, validation_data = (X_test, y_test), class_weight=classWeight)

After some examination, I found that the issue was the data itself. It was very dirty as in same input had 2 different outputs, hence creating confusion. After clearing up the data now my accuracy goes up to %69. Still not enough to be good, but at least I can now work my way up from here now that the data is clear.

I used the below code to test:

import os
import sys

import pandas as pd
import numpy as np

from keras.models import Sequential
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.core import Dense, Activation, Dropout, Flatten
from keras.utils import np_utils

sys.path.append("./")
import AudioProcessing as ap
import ImageTools as it


# input image dimensions
img_rows, img_cols = 28, 28
dim = 1
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
nb_pool = 2
# convolution kernel size
nb_conv = 3

batch_size = 128
nb_classes = 2
nb_epoch = 200

for i in range(20):
    print "\n"

## Generate spectrograms if necessary
if(len(os.listdir("./AudioNormalPathalogicClassification/Image")) > 0):
    print "Audio files are already processed. Skipping..."
else:
    # Read the result csv
    df = pd.read_csv('./AudioNormalPathalogicClassification/Result/AudioNormalPathalogicClassification_result.csv', header = None, encoding = "utf-8")

    df.columns = ["RegionName","Filepath","IsNormal"]

    bool_mapping = {True : 1, False : 0}

    for col in df:
        if(col == "RegionName" or col == "Filepath"):
            a = 3      
        else:
            df[col] = df[col].map(bool_mapping)

    region_names = df.iloc[:,0].values
    filepaths = df.iloc[:,1].values
    y = df.iloc[:,2].values
    #Generate spectrograms and make a new CSV file
    print "Generating spectrograms for the audio files..."
    result = ap.audio_2_image(filepaths, region_names, y, "./AudioNormalPathalogicClassification/Image/", ".png",(img_rows,img_cols))
    df = pd.DataFrame(data = result)
    df.to_csv("NormalVsPathalogic.csv",header= False, index = False, encoding = "utf-8")

# Load images into memory
print "Loading images into memory..."
df = pd.read_csv('NormalVsPathalogic.csv', header = None, encoding = "utf-8")
y = df.iloc[:,0].values
y = np_utils.to_categorical(y, nb_classes)
y = np.asarray(y)

X = df.iloc[:,1:].values
X = np.asarray(X)
X = X.reshape(X.shape[0], dim, img_rows, img_cols)
X = X.astype("float32")
X /= 255

print X.shape

model = Sequential()

model.add(Convolution2D(64, nb_conv, nb_conv,
                        border_mode='valid',
                        input_shape=(1, img_rows, img_cols)))

model.add(Activation('relu'))

model.add(Convolution2D(32, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))

model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(128))
model.add(Activation('relu'))

model.add(Dropout(0.5))

model.add(Dense(nb_classes))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adadelta')

print model.summary()

model.fit(X, y, batch_size = batch_size, nb_epoch = nb_epoch, show_accuracy = True, verbose = 1)

Check out this one

sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model.compile( loss = "categorical_crossentropy", 
               optimizer = sgd, 
               metrics=['accuracy']
             )

Check out the documentation

I had better results with MNIST

By mistake I had added a softmax at the end instead of sigmoid. Try doing the latter. It worked as expected when I did this. For one output layer, softmax always gives values of 1 and this is what had happened.

I faced a similar issue. One-hot encoding the target variable using nputils in Keras, solved the issue of accuracy and validation loss being stuck. Using weights for balancing the target classes further improved performance.

Solution :

from keras.utils.np.utils import to_categorical
y_train = to_categorical(y_train)
y_val = to_categorical(y_val) 

I've the same problem as you my solution was a loop instead of epochs

for i in range(10): history = model.fit_generator(generator=training_generator, validation_data=validation_generator, use_multiprocessing=True, workers=6, epochs=1)

for i in range(10): history = model.fit_generator(generator=training_generator, validation_data=validation_generator, use_multiprocessing=True, workers=6, epochs=1) #save model model.save('drive/My Drive/vggnet10epochs.h5') model = load_model('drive/My Drive/vggnet10epochs.h5')

I got 13% Accuracy increment using this 'sigmoid' activation

model = Sequential()
model.add(Dense(3072, input_shape=(3072,), activation="sigmoid"))
model.add(Dense(512, activation="sigmoid"))
model.add(Dense(1, activation="sigmoid"))

Or you can also test the following, where 'relu' in first and hidden layer.

model = Sequential()
model.add(Dense(3072, input_shape=(3072,), activation="relu"))
model.add(Dense(512, activation="sigmoid"))
model.add(Dense(1, activation="sigmoid"))

As mentioned above, the problem mainly arises from the type of optimizers chosen. However, it can also be driven from the fact of topping 2 Dense layers with the same activation functions(softmax, for example). In this case, NN finds a local minimum and is not able to descent more from that point, rolling around the same acc (val_acc) values. Hope it helps out.

I had similar problem. I had binary class which was labeled by 1 and 2. After testing different kinds of optimizer and activation functions I found that the root of the problem was my labeling to classes. In the other words I changed the labels to 0 and 1 instead of 1 and 2, then this problem solved!

I faced same problem for multi-class, Try to changing optimizer by default it is Adam change it to sgd.

model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])

you can also try different Activation functions eg. (relu, sigmoid, softmax, softplus, etc.)

Some imp links

Optimizers

Activations

As pointed out by others, the optimizer probably doesn't suit your data/model which stuck in local minima. A neural network should at least be able to overfit the data (training_acc close to 1). I once had a similar problem. I solved by trying different optimizers (in my case from SGD to RMSprop)

In my case, my problem was binary and I was using the 'softmax' activation function and it doesn't work. I changed to 'sigmoid' it works properly for me.

I had the exactly same problem: validation loss and accuracy remaining the same through the epochs. I increased the batch size 10x times, reduced learning rate by 100x times, etc. It did not work.

My last try, inspired by monolingual's and Ranjab's answers, worked.

my solution was to add Batchnormalization AND arrange the order as below:

Conv - DropOut - BatchNorm - Activation - Pool.

as recommended in Ordering of batch normalization and dropout?.

I know this is an old question but as of today (14/06/2021), the comment from @theTechGuy works well on tf 2.3. The code is:

    from tensorflow.keras.optimizers import SGD

    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

    model.compile( loss = "categorical_crossentropy", 
                   optimizer = sgd, 
                   metrics=['accuracy']
                 )

I tried playing a lot with the optimizers and activation functions, but the only thing that worked was Batchnormalization1. And I guess it is a good practice too. You can import it as:

from tensorflow.keras.layers import BatchNormalization

and simply add it before each hidden layer:

model.add(BatchNormalization())

I had the same problem, but in my case, it was caused by a non-regularized column on my data. This column had huge value. Fixing that solved it for me.

So, I just converted it to values around 0 and 1.