I am trying to create a custom layer for multiclass classification problem in a Tabular dataset using 1d-cnn. my original dataset has ~20000 features and ~5000000 rows with 8 classes. It suffers from severe imbalance, so I want to add attention layer to the 1D-CNNs such that the minority classes are paid attention to. To simplify the problem I will show the model I built on Iris Dataset. I would like to check if what I am doing is correct, the aim is to add attention to 1D-cnn layers in a VGG variant model. I would appreciate any help and input.
# Load libraries
import sklearn
from sklearn.datasets import load_iris
import numpy as np
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
## Plotting Library
import matplotlib
import matplotlib.pyplot as plt
## File I/O
import os
## ML & DeepL libraries:
import tensorflow.keras
from tensorflow.keras.utils import to_categorical
import np_utils # This library has been separated from keras and tensorflow.
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense, Activation, Flatten, Conv1D, Dropout, BatchNormalization, MaxPooling1D, LeakyReLU
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint
from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit
## ML & DL Model Evaluaiton Libraries_Classes:
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
from sklearn.utils.class_weight import compute_class_weight
# preprocessing libraries:
from sklearn.preprocessing import minmax_scale
# Compute sample weights using sklearn:
from sklearn.utils.class_weight import compute_sample_weight
from tensorflow.keras import Model
from tensorflow.keras.layers import Layer
import tensorflow.keras.backend as K
from tensorflow.keras.layers import Input, Dense
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
# load Iris
data = load_iris()
# Get the X features and corresponding labels:
X_ = data['data']
y_ = data['target']
X_ = X_[0:]
y_ = y_[0:]
Xtrain, Xtest, ytrain, ytest = train_test_split(X_, y_, test_size=0.33, random_state=42)
Creat attention layer:
# Add attention layer to the deep learning network
class attention(Layer):
def __init__(self,**kwargs):
super(attention,self).__init__(**kwargs)
def build(self,input_shape):
self.W=self.add_weight(name='attention_weight',
shape=(input_shape[-1],1),
initializer='random_normal',
trainable=True)
self.b=self.add_weight(name='attention_bias',
shape=(input_shape[1],1),
initializer='zeros',
trainable=True)
super(attention, self).build(input_shape)
def call(self,x):
# Alignment scores. Pass them through tanh function
e = K.tanh(K.dot(x,self.W)+self.b)
# Remove dimension of size 1
e = K.squeeze(e, axis=-1)
# Compute the weights
alpha = K.softmax(e)
# Reshape to tensorFlow format
alpha = K.expand_dims(alpha, axis=-1)
# Compute the context vector
context = x * alpha
context = K.sum(context, axis=1)
return context
Create 1D-CNN with attention:
hidden_units = 2
epochs = 40
activation = ['tanh', 'tanh']
def create_cnn_with_attention(hidden_units,
dense_units,
input_shape,
activation):
x=Input(shape=input_shape)
cnn_layer = Conv1D(filters=64,
kernel_size =21,
strides =1,
padding ='same',
activation ='relu',
input_shape = input_shape,
kernel_initializer= tensorflow.keras.initializers.he_normal())(x)
attention_layer = attention()(cnn_layer)
outputs=Dense(dense_units,
trainable=True,
activation=activation)(attention_layer)
model=Model(x,outputs)
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics = ['sparse_categorical_accuracy'])
return model
model_attention = create_cnn_with_attention(hidden_units=hidden_units,
dense_units=3,
input_shape = (input_shape),
activation='tanh')
model_attention.summary()
Model: "model_12"
Layer (type) Output Shape Param #
input_14 (InputLayer) [(None, 4, 1)] 0
conv1d_18 (Conv1D) (None, 4, 64) 1408
attention_12 (attention) (None, 64) 68
dense_14 (Dense) (None, 3) 195
Total params: 1,671
Trainable params: 1,671
Non-trainable params: 0
model_attention.fit(Xtrain, ytrain, epochs = 40)
Epoch 1/40
4/4 [==============================] - 0s 5ms/step - loss: 11.0361 - sparse_categorical_accuracy: 0.3100
Epoch 2/40
4/4 [==============================] - 0s 2ms/step - loss: 10.8962 - sparse_categorical_accuracy: 0.3100
Epoch 3/40
4/4 [==============================] - 0s 2ms/step - loss: 10.6803 - sparse_categorical_accuracy: 0.1000
Epoch 4/40
4/4 [==============================] - 0s 1ms/step - loss: 2.7534 - sparse_categorical_accuracy: 0.0000e+00
Epoch 5/40
4/4 [==============================] - 0s 1ms/step - loss: 1.0986 - sparse_categorical_accuracy: 0.0000e+00
Epoch 6/40
4/4 [==============================] - 0s 2ms/step - loss: 1.0986 - sparse_categorical_accuracy: 0.0000e+00
Epoch 7/40
4/4 [==============================] - 0s 2ms/step - loss: 1.0986 - sparse_categorical_accuracy: 0.0000e+00
Epoch 8/40
4/4 [==============================] - 0s 2ms/step - loss: 1.0986 - sparse_categorical_accuracy: 0.0000e+00 ....
train_mse_attn = model_attention.evaluate(Xtrain, ytrain)
4/4 [==============================] - 0s 2ms/step - loss: 1.0986 - sparse_categorical_accuracy: 0.0000e+00
I have a few thousand audio files and I want to classify them using Keras and Theano. So far, I generated a 28x28 spectrograms (bigger is probably better, but I am just trying to get the algorithm work at this point) of each audio file and read the image into a matrix. So in the end I get this big image matrix to feed into the network for image classification.
In a tutorial I found this mnist classification code:
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense
from keras.utils import np_utils
batch_size = 128
nb_classes = 10
nb_epochs = 2
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
X_train /= 255
X_test /= 255
print(X_train.shape[0], "train samples")
print(X_test.shape[0], "test samples")
y_train = np_utils.to_categorical(y_train, nb_classes)
y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(Dense(output_dim = 100, input_dim = 784, activation= "relu"))
model.add(Dense(output_dim = 200, activation = "relu"))
model.add(Dense(output_dim = 200, activation = "relu"))
model.add(Dense(output_dim = nb_classes, activation = "softmax"))
model.compile(optimizer = "adam", loss = "categorical_crossentropy")
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))
score = model.evaluate(X_test, y_test, show_accuracy = True, verbose = 0)
print("Test score: ", score[0])
print("Test accuracy: ", score[1])
This code runs, and I get the result as expected:
(60000L, 'train samples')
(10000L, 'test samples')
Train on 60000 samples, validate on 10000 samples
Epoch 1/2
2s - loss: 0.2988 - acc: 0.9131 - val_loss: 0.1314 - val_acc: 0.9607
Epoch 2/2
2s - loss: 0.1144 - acc: 0.9651 - val_loss: 0.0995 - val_acc: 0.9673
('Test score: ', 0.099454972004890438)
('Test accuracy: ', 0.96730000000000005)
Up to this point everything runs perfectly, however when I apply the above algorithm to my dataset, accuracy gets stuck.
My code is as follows:
import os
import pandas as pd
from sklearn.cross_validation import train_test_split
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
import AudioProcessing as ap
import ImageTools as it
batch_size = 128
nb_classes = 2
nb_epoch = 10
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:
print "Generating spectrograms for the audio files..."
ap.audio_2_image("./AudioNormalPathalogicClassification/Audio/","./AudioNormalPathalogicClassification/Image/",".wav",".png",(28,28))
# Read the result csv
df = pd.read_csv('./AudioNormalPathalogicClassification/Result/result.csv', header = None)
df.columns = ["RegionName","IsNormal"]
bool_mapping = {True : 1, False : 0}
nb_classes = 2
for col in df:
if(col == "RegionName"):
a = 3
else:
df[col] = df[col].map(bool_mapping)
y = df.iloc[:,1:].values
y = np_utils.to_categorical(y, nb_classes)
# Load images into memory
print "Loading images into memory..."
X = it.load_images("./AudioNormalPathalogicClassification/Image/",".png")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)
X_train = X_train.reshape(X_train.shape[0], 784)
X_test = X_test.reshape(X_test.shape[0], 784)
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
X_train /= 255
X_test /= 255
print("X_train shape: " + str(X_train.shape))
print(str(X_train.shape[0]) + " train samples")
print(str(X_test.shape[0]) + " test samples")
model = Sequential()
model.add(Dense(output_dim = 100, input_dim = 784, activation= "relu"))
model.add(Dense(output_dim = 200, activation = "relu"))
model.add(Dense(output_dim = 200, activation = "relu"))
model.add(Dense(output_dim = nb_classes, activation = "softmax"))
model.compile(loss = "categorical_crossentropy", optimizer = "adam")
print model.summary()
model.fit(X_train, y_train, batch_size = batch_size, nb_epoch = nb_epoch, show_accuracy = True, verbose = 1, validation_data = (X_test, y_test))
score = model.evaluate(X_test, y_test, show_accuracy = True, verbose = 1)
print("Test score: ", score[0])
print("Test accuracy: ", score[1])
AudioProcessing.py
import os
import scipy as sp
import scipy.io.wavfile as wav
import matplotlib.pylab as pylab
import Image
def save_spectrogram_scipy(source_filename, destination_filename, size):
dt = 0.0005
NFFT = 1024
Fs = int(1.0/dt)
fs, audio = wav.read(source_filename)
if(len(audio.shape) >= 2):
audio = sp.mean(audio, axis = 1)
fig = pylab.figure()
ax = pylab.Axes(fig, [0,0,1,1])
ax.set_axis_off()
fig.add_axes(ax)
pylab.specgram(audio, NFFT = NFFT, Fs = Fs, noverlap = 900, cmap="gray")
pylab.savefig(destination_filename)
img = Image.open(destination_filename).convert("L")
img = img.resize(size)
img.save(destination_filename)
pylab.clf()
del img
def audio_2_image(source_directory, destination_directory, audio_extension, image_extension, size):
nb_files = len(os.listdir(source_directory));
count = 0
for file in os.listdir(source_directory):
if file.endswith(audio_extension):
destinationName = file[:-4]
save_spectrogram_scipy(source_directory + file, destination_directory + destinationName + image_extension, size)
count += 1
print ("Generating spectrogram for files " + str(count) + " / " + str(nb_files) + ".")
ImageTools.py
import os
import numpy as np
import matplotlib.image as mpimg
def load_images(source_directory, image_extension):
image_matrix = []
nb_files = len(os.listdir(source_directory));
count = 0
for file in os.listdir(source_directory):
if file.endswith(image_extension):
with open(source_directory + file,"r+b") as f:
img = mpimg.imread(f)
img = img.flatten()
image_matrix.append(img)
del img
count += 1
#print ("File " + str(count) + " / " + str(nb_files) + " loaded.")
return np.asarray(image_matrix)
So I run the above code and recieve:
Audio files are already processed. Skipping...
Loading images into memory...
X_train shape: (2394L, 784L)
2394 train samples
1027 test samples
--------------------------------------------------------------------------------
Initial input shape: (None, 784)
--------------------------------------------------------------------------------
Layer (name) Output Shape Param #
--------------------------------------------------------------------------------
Dense (dense) (None, 100) 78500
Dense (dense) (None, 200) 20200
Dense (dense) (None, 200) 40200
Dense (dense) (None, 2) 402
--------------------------------------------------------------------------------
Total params: 139302
--------------------------------------------------------------------------------
None
Train on 2394 samples, validate on 1027 samples
Epoch 1/10
2394/2394 [==============================] - 0s - loss: 0.6898 - acc: 0.5455 - val_loss: 0.6835 - val_acc: 0.5716
Epoch 2/10
2394/2394 [==============================] - 0s - loss: 0.6879 - acc: 0.5522 - val_loss: 0.6901 - val_acc: 0.5716
Epoch 3/10
2394/2394 [==============================] - 0s - loss: 0.6880 - acc: 0.5522 - val_loss: 0.6842 - val_acc: 0.5716
Epoch 4/10
2394/2394 [==============================] - 0s - loss: 0.6883 - acc: 0.5522 - val_loss: 0.6829 - val_acc: 0.5716
Epoch 5/10
2394/2394 [==============================] - 0s - loss: 0.6885 - acc: 0.5522 - val_loss: 0.6836 - val_acc: 0.5716
Epoch 6/10
2394/2394 [==============================] - 0s - loss: 0.6887 - acc: 0.5522 - val_loss: 0.6832 - val_acc: 0.5716
Epoch 7/10
2394/2394 [==============================] - 0s - loss: 0.6882 - acc: 0.5522 - val_loss: 0.6859 - val_acc: 0.5716
Epoch 8/10
2394/2394 [==============================] - 0s - loss: 0.6882 - acc: 0.5522 - val_loss: 0.6849 - val_acc: 0.5716
Epoch 9/10
2394/2394 [==============================] - 0s - loss: 0.6885 - acc: 0.5522 - val_loss: 0.6836 - val_acc: 0.5716
Epoch 10/10
2394/2394 [==============================] - 0s - loss: 0.6877 - acc: 0.5522 - val_loss: 0.6849 - val_acc: 0.5716
1027/1027 [==============================] - 0s
('Test score: ', 0.68490593621422047)
('Test accuracy: ', 0.57156767283349563)
I tried changing the network, adding more epochs, but I always get the same result no matter what. I don't understand why I am getting the same result.
Any help would be appreciated. Thank you.
Edit:
I found a mistake where pixel values were not read correctly. I fixed the ImageTools.py below as:
import os
import numpy as np
from scipy.misc import imread
def load_images(source_directory, image_extension):
image_matrix = []
nb_files = len(os.listdir(source_directory));
count = 0
for file in os.listdir(source_directory):
if file.endswith(image_extension):
with open(source_directory + file,"r+b") as f:
img = imread(f)
img = img.flatten()
image_matrix.append(img)
del img
count += 1
#print ("File " + str(count) + " / " + str(nb_files) + " loaded.")
return np.asarray(image_matrix)
Now I actually get grayscale pixel values from 0 to 255, so now my dividing it by 255 makes sense. However, I still get the same result.
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)
and you can as well save the model each epoch so you can pause the training after any epoch you want
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.
I had the same problem. My solution was to change the last layer activation function from "softmax" to "sigmoid" since i was dealing with a binary classification problem.
model.add(layers.Dense(1, activation="sigmoid"))