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Accuracy is not increasing in images classification when using TensorFlow model

At my job Interview yesterday, I was asked to build a neural network using TesnorFlow in python to classify images from the flowers images dataset.
But even though it should've worked theoretically, for some reason I couldn't increase the accuracy above 20s%.
Python Version: 3.8.13
TensorFlow Versioin: 2.4.1
The data preprocessing methods from the interviewer were given as follows:
# create datase
IMG_SIZE = 160
BATCH_SIZE = 32
AUTOTUNE = tf.data.experimental.AUTOTUNE
def _parse_data(x,y):
image = tf.io.read_file(x)
image = tf.image.decode_jpeg(image, channels=3)
image = tf.cast(image, dtype=tf.float32)
image = tf.math.l2_normalize(image)
image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))
return image,y
def _input_fn(x,y):
ds = tf.data.Dataset.from_tensor_slices((x,y))
ds = ds.map(_parse_data)
ds = ds.shuffle(buffer_size=data_size)
ds = ds.repeat()
ds = ds.batch(BATCH_SIZE)
ds = ds.prefetch(buffer_size=AUTOTUNE)
return ds
train_ds = _input_fn(x_train, y_train)
validation_ds = _input_fn(x_valid, y_valid)
With both training and validation datasets being
<PrefetchDataset shapes: ((None, 160, 160, 3), (None,)), types:
(tf.float32, tf.int32)>
With the network being as follows:
from tensorflow.keras import datasets, layers, models
model_seq = models.Sequential()
model_seq.add(layers.experimental.preprocessing.RandomFlip("horizontal",input_shape=(IMG_SIZE,IMG_SIZE,3)))
model_seq.add(layers.experimental.preprocessing.RandomRotation(0.2))
model_seq.add(layers.experimental.preprocessing.Rescaling(1./255))
model_seq.add(layers.Conv2D(16, 3, padding='same', activation='relu'))
model_seq.add(layers.MaxPooling2D())
model_seq.add(layers.Conv2D(32, 3, padding='same', activation='relu'))
model_seq.add(layers.MaxPooling2D())
model_seq.add(layers.Conv2D(64, 3, padding='same', activation='relu'))
model_seq.add(layers.MaxPooling2D())
model_seq.add(layers.Dropout(0.2))
model_seq.add(layers.Flatten())
model_seq.add(layers.Dense(128, activation='relu'))
model_seq.add(layers.Dense(len(label_names), activation='softmax'))
model_seq.summary()
The output layer being the only thing that isn't allowed to be change.
model_seq.add(layers.Dense(len(label_names), activation='softmax'))
(Please note I was for some reason asked to use model_seq.add(), and even though it could be triggering for some of you, please ignore it this once :) )
For compiling the model, I used the following:
model_seq.compile(optimizer="Adam",
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
And for fitting the model:
history = model_seq.fit(train_ds,epochs=20,
validation_data = validation_ds,
steps_per_epoch=100,validation_steps=100)
The things I've tried:
Using different Augmentation methods (or removing the whole section
from the network).
Changing the Batch and Image sizes.
Using Dropout layers.
Using early stopping as follows:
callback = tf.keras.callbacks.EarlyStopping(
monitor='val_loss',
min_delta=0, patience=3, verbose=0,
mode='auto',baseline=None,
restore_best_weights=True)
history = model_seq.fit(train_ds,epochs=20,
validation_data = validation_ds,
steps_per_epoch=100,validation_steps=100,
callbacks = [callback])
Yet despite all of the above, I couldn't get any results. Since I couldn't find out what I did wrong exactly, I'm hoping someone here could tell me, so I could learn from this experience.
(Please take into consideration that I wasn't allowed to change the preprocessing functions, with the parameters IMG_SIZE and BATCH_SIZE being the only exception).

“TLDR: If you want to use their preprocessing part and don't change anything go to First Approach. If you want to augment images, Go to Second Approach and use ImageDataGenerator”.
First Approach
As you say: I had to use the preprocessing functions and don't change this and because I don't access your data, I use cifar10 dataset and use your preprocessing part. (only line of reading from file changed).
Because you shouldn't change IMG_SIZE=160 in the preprocessing part, I add this layer : tf.keras.layers.Lambda(lambda image: tf.image.resize(image, (32, 32)))) to the network because working with large images causes a crash.
You don't need a very large network, first check with a small network then step by step add parameters then add layers.
We can get a better result like the below: (On cifar10 with your network I get 10% accuracy like you.)
import tensorflow as tf
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()
# create datase
IMG_SIZE = 160
BATCH_SIZE = 32
data_size = 32
AUTOTUNE = tf.data.experimental.AUTOTUNE
def _parse_data(x,y):
# image = tf.io.read_file(x) <- because don't read from path
image = x
# image = tf.image.decode_jpeg(image, channels=3) <- because don't read from path and don't have jpeg
image = tf.cast(image, dtype=tf.float32)
image = tf.math.l2_normalize(image)
image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))
return image,y
def _input_fn(x,y):
ds = tf.data.Dataset.from_tensor_slices((x,y))
ds = ds.map(_parse_data)
ds = ds.shuffle(buffer_size=data_size)
ds = ds.repeat()
ds = ds.batch(BATCH_SIZE)
ds = ds.prefetch(buffer_size=AUTOTUNE)
return ds
train_ds = _input_fn(X_train, y_train)
validation_ds = _input_fn(X_test, y_test)
model = tf.keras.Sequential([
tf.keras.Input(shape=(160, 160, 3)),
tf.keras.layers.Lambda(lambda image: tf.image.resize(image, (32, 32))),
tf.keras.layers.Conv2D(filters=32,kernel_size=(3,3),activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(50,activation='relu'),
tf.keras.layers.Dense(10,activation='softmax')
])
model.compile(optimizer="Adam",
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['accuracy'])
history = model.fit(train_ds,epochs=4,
validation_data = validation_ds,
steps_per_epoch=100,validation_steps=100)
Output:
Epoch 1/4
100/100 [==============================] - 8s 38ms/step - loss: 2.2445 - accuracy: 0.1375 - val_loss: 2.1406 - val_accuracy: 0.2138
Epoch 2/4
100/100 [==============================] - 3s 33ms/step - loss: 2.0552 - accuracy: 0.2688 - val_loss: 1.9764 - val_accuracy: 0.3250
Epoch 3/4
100/100 [==============================] - 4s 38ms/step - loss: 1.9468 - accuracy: 0.3022 - val_loss: 1.9014 - val_accuracy: 0.3200
Epoch 4/4
100/100 [==============================] - 4s 36ms/step - loss: 1.8936 - accuracy: 0.3341 - val_loss: 1.8883 - val_accuracy: 0.3419
Second Approach: Augment images with ImageDataGenerator:
import tensorflow as tf
flowers = tf.keras.utils.get_file(
'flower_photos',
'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
untar=True)
data_gen = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1./255,
rotation_range = 10, # Degree range for random rotations.
horizontal_flip = True, # Randomly flip inputs horizontally.
vertical_flip = True, # Randomly flip inputs vertically.
)
imgs_dataset = data_gen.flow_from_directory(flowers, class_mode='categorical',
target_size=(160, 160), batch_size=32,
shuffle=True)
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(filters=32,kernel_size=(3,3),input_shape=(160, 160, 3),activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(50,activation='relu'),
tf.keras.layers.Dense(5,activation='softmax')
])
model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])
model.fit(imgs_dataset,epochs=5)
Output:
Found 3670 images belonging to 5 classes.
Epoch 1/5
115/115 [==============================] - 39s 311ms/step - loss: 1.7904 - accuracy: 0.4161
Epoch 2/5
115/115 [==============================] - 27s 236ms/step - loss: 1.0878 - accuracy: 0.5605
Epoch 3/5
115/115 [==============================] - 28s 244ms/step - loss: 1.0252 - accuracy: 0.6005
Epoch 4/5
115/115 [==============================] - 27s 233ms/step - loss: 0.9735 - accuracy: 0.6196
Epoch 5/5
115/115 [==============================] - 29s 248ms/step - loss: 0.9313 - accuracy: 0.6455

WARNING:tensorflow:Model was constructed with shape (20, 37, 42) for input Tensor("input_5:0", shape=(20, 37, 42), dtype=float32), but

WARNING:tensorflow:Model was constructed with shape (20, 37, 42) for input Tensor("input_5:0", shape=(20, 37, 42), dtype=float32), but it was called on an input with incompatible shape (None, 37).
Hello! Deep learning noob here... I'm having trouble using LSTM layers.
The input is a length 37 float array containing 2 floats and a length 35 one-hot array converted into float. The output is a length 19 array with 0s and 1s. Like the title suggests, I'm having trouble reshaping my input data to fit the model, and I'm not even sure what input dimensions would be considered 'compatible'
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import random
inputs, outputs = [], []
for x in range(10000):
tempi, tempo = [], []
tempi.append(random.random() - 0.5)
tempi.append(random.random() - 0.5)
for x2 in range(35):
if random.random() > 0.5:
tempi.append(1.)
else:
tempi.append(0.)
for x2 in range(19):
if random.random() > 0.5:
tempo.append(1.)
else:
tempo.append(0.)
inputs.append(tempi)
outputs.append(tempo)
batch = 20
timesteps = 42
training_units = 0.85
cutting_point_i = int(len(inputs)*training_units)
cutting_point_o = int(len(outputs)*training_units)
x_train, x_test = np.asarray(inputs[:cutting_point_i]), np.asarray(inputs[cutting_point_i:])
y_train, y_test = np.asarray(outputs[:cutting_point_o]), np.asarray(outputs[cutting_point_o:])
input_layer = keras.Input(shape=(37,timesteps),batch_size=batch)
dense = layers.LSTM(150, activation="sigmoid", return_sequences=True)
x = dense(input_layer)
hidden_layer_2 = layers.LSTM(150, activation="sigmoid", return_sequences=True)(x)
output_layer = layers.Dense(10, activation="softmax")(hidden_layer_2)
model = keras.Model(inputs=input_layer, outputs=output_layer, name="my_model"

Several problems here.
Your input didn't have time steps, you need input shape (n, time steps, features)
In input_shape, the time steps dimension comes first, not last
Your last LSTM layer returned sequences, so you can't compare it with 0s and 1s
What I did:
I added time steps to your data (7)
I permuted the dimensions in input_shape
I set the final return_sequences=False
Completely fixed example with generated data:
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
batch = 20
n_samples = 1000
timesteps = 7
features = 10
x_train = np.random.rand(n_samples, timesteps, features)
y_train = keras.utils.to_categorical(np.random.randint(0, 10, n_samples))
input_layer = keras.Input(shape=(timesteps, features),batch_size=batch)
dense = layers.LSTM(16, activation="sigmoid", return_sequences=True)(input_layer)
hidden_layer_2 = layers.LSTM(16, activation="sigmoid", return_sequences=False)(dense)
output_layer = layers.Dense(10, activation="softmax")(hidden_layer_2)
model = keras.Model(inputs=input_layer, outputs=output_layer, name="my_model")
model.compile(loss='categorical_crossentropy', optimizer='adam')
history = model.fit(x_train, y_train)
Train on 1000 samples
20/1000 [..............................] - ETA: 2:50 - loss: 2.5145
200/1000 [=====>........................] - ETA: 14s - loss: 2.3934
380/1000 [==========>...................] - ETA: 5s - loss: 2.3647
560/1000 [===============>..............] - ETA: 2s - loss: 2.3549
740/1000 [=====================>........] - ETA: 1s - loss: 2.3395
900/1000 [==========================>...] - ETA: 0s - loss: 2.3363
1000/1000 [==============================] - 4s 4ms/sample - loss: 2.3353

The correct input for your model is (20, 37, 42).
Note: Here 20 is the batch_size you have explicitly specified.
Code:
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
batch = 20
timesteps = 42
training_units = 0.85
x1 = tf.constant(np.random.randint(50, size =(1000,37, 42)), dtype = tf.float32)
y1 = tf.constant(np.random.randint(10, size =(1000,)), dtype = tf.int32)
input_layer = keras.Input(shape=(37,timesteps),batch_size=batch)
dense = layers.LSTM(150, activation="sigmoid", return_sequences=True)
x = dense(input_layer)
hidden_layer_2 = layers.LSTM(150, activation="sigmoid", return_sequences=True)(x)
hidden_layer_3 = layers.Flatten()(hidden_layer_2)
output_layer = layers.Dense(10, activation="softmax")(hidden_layer_3)
model = keras.Model(inputs=input_layer, outputs=output_layer, name="my_model")
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
tf.keras.utils.plot_model(model, 'my_first_model.png', show_shapes=True)
Model Architecture:
You can clearly see the Input Size.
Code to Run:
model.fit(x = x1, y = y1, batch_size = batch, epochs = 10)
Note: Whatever batch_size you have specified you have to specify the same batch_size in the model.fit() command.
Output:
Epoch 1/10
50/50 [==============================] - 4s 89ms/step - loss: 2.3288 - accuracy: 0.0920
Epoch 2/10
50/50 [==============================] - 5s 91ms/step - loss: 2.3154 - accuracy: 0.1050
Epoch 3/10
50/50 [==============================] - 5s 101ms/step - loss: 2.3114 - accuracy: 0.0900
Epoch 4/10
50/50 [==============================] - 5s 101ms/step - loss: 2.3036 - accuracy: 0.1060
Epoch 5/10
50/50 [==============================] - 5s 99ms/step - loss: 2.2998 - accuracy: 0.1000
Epoch 6/10
50/50 [==============================] - 4s 89ms/step - loss: 2.2986 - accuracy: 0.1170
Epoch 7/10
50/50 [==============================] - 4s 84ms/step - loss: 2.2981 - accuracy: 0.1300
Epoch 8/10
50/50 [==============================] - 5s 103ms/step - loss: 2.2950 - accuracy: 0.1290
Epoch 9/10
50/50 [==============================] - 5s 106ms/step - loss: 2.2960 - accuracy: 0.1210
Epoch 10/10
50/50 [==============================] - 5s 97ms/step - loss: 2.2874 - accuracy: 0.1210

LSTM model on the 3 class label as classification problem

My problem is to predict the output as which has 3 class label,
Lets say I have 20000 samples in my dataset with each sample is associated with label (0,1,2).
As this is multiclass classification problem.
Can I only give input as Labels which are ( 0, 1,2) to the network and get prediction based on the labels.
Will the data feeded to the network is sufficient to learn and predict the output
Please help me with your inputs
# Below is the code
X_train, X_test, y_train, y_test = train_test_split(values_train[:, 0],
values_train[:, 1],
test_size=0.25,
random_state=42)
print(" X Training Set size is",X_train.shape )
print(" y Training Set size is",y_train.shape )
print(" X Test Set size is",X_test.shape)
print(" y Test Set size is",y_test.shape )
'X Training Set size is (165081,)'
'y Training Set size is (165081,)'
'X Test Set size is (55028,)'
'y Test Set size is (55028,)'
# convert to LSTM friendly format
X_train = X_train.reshape(len(X_train),1, 1)
X_test = X_test.reshape(len(X_test),1,1)
print(X_train.shape, X_test.shape)
(165081, 1, 1) (55028, 1, 1)
# configure network
n_batch = 1
n_epoch = 100
n_neurons = 10
from keras.optimizers import SGD
opt = SGD(lr=0.01)
# design network
model = Sequential()
model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X_train.shape[1],
X_train.shape[2]),
stateful=True))
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
# fit network
for i in range(n_epoch):
model.fit(X_train, y_train ,validation_data=(X_test, y_test),
epochs=1, batch_size=n_batch, verbose=1, shuffle= False)
model.reset_states()
df_actual = []
dp_predict = []
for i in range(len(X_test)):
testX,testy = X_test[i],y_test[i]
testX = testX.reshape(1, 1, 1)
yhat = model.predict(testX, batch_size=1)
df_actual.append(testy)
dp_predict.append(yhat)
print('>Actual =%.1f, Predicted=%.1f' % (testy, yhat))
I am not able to get correct prediction in this model.
Update:
Please find the below Validation accuracy and Training accuracy with the loss
Train on 154076 samples, validate on 66033 samples
Epoch 1/5
154076/154076 [==============================] - 289s 2ms/step - loss: 1.0033 - accuracy: 0.3816 - val_loss: 1.0018 - val_accuracy: 0.4286
Epoch 2/5
154076/154076 [==============================] - 291s 2ms/step - loss: 1.0021 - accuracy: 0.3817 - val_loss: 1.0020 - val_accuracy: 0.4286
Epoch 3/5
154076/154076 [==============================] - 293s 2ms/step - loss: 1.0018 - accuracy: 0.3804 - val_loss: 1.0014 - val_accuracy: 0.4286
Epoch 4/5
154076/154076 [==============================] - 290s 2ms/step - loss: 1.0016 - accuracy: 0.3812 - val_loss: 1.0012 - val_accuracy: 0.4286
Epoch 5/5
154076/154076 [==============================] - 290s 2ms/step - loss: 1.0015 - accuracy: 0.3814 - val_loss: 1.0012 - val_accuracy: 0.4286
Can anyone suggest me what can be improvement
Note: - I have normalized the input data with MinMaxScalar and used the scaled data, but there is no change in the output

Class labels are of categorical type. Neural networks can't learn on categorical data. You have to one-hot encode it with e.g. keras.utils.to_categorical:
x = values_train[:, 0]
y = values_train[:, 1]
y = keras.utils.to_categorical(y)
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)

Calculating the Accuracy of A Keras Neural Network in Python

I have created a Keras neural network. The neural network was trained during eight epochs, and it outputs this loss value and accuracy:
Epoch 1/8
2009/2009 [==============================] - 0s 177us/step - loss: 0.0824 - acc: 4.9776e-04
Epoch 2/8
2009/2009 [==============================] - 0s 34us/step - loss: 0.0080 - acc: 4.9776e-04
Epoch 3/8
2009/2009 [==============================] - 0s 37us/step - loss: 0.0071 - acc: 4.9776e-04
Epoch 4/8
2009/2009 [==============================] - 0s 38us/step - loss: 0.0071 - acc: 4.9776e-04
Epoch 5/8
2009/2009 [==============================] - 0s 35us/step - loss: 0.0070 - acc: 4.9776e-04
Epoch 6/8
2009/2009 [==============================] - 0s 38us/step - loss: 0.0071 - acc: 4.9776e-04
Epoch 7/8
2009/2009 [==============================] - 0s 36us/step - loss: 0.0068 - acc: 4.9776e-04
Epoch 8/8
2009/2009 [==============================] - 0s 40us/step - loss: 0.0070 - acc: 4.9776e-04
How do I interpret the loss function provided within the output?
Is there any way to find the variation percentage between the actual price and prediction for every single day in the data set?
Here is the neural network:
import tensorflow as tf
import keras
import numpy as np
#import quandle
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import sklearn
import math
import pandas_datareader as web
def func_stock_prediction(stockdata, start, end):
start = start
end = end
df = web.DataReader(stockdata, "yahoo", start, end)
df = df[['Close']]
previous = 5
def create_dataset(df, previous):
dataX, dataY = [], []
for i in range(len(df)-previous-1):
a = df[i:(i+previous), 0]
dataX.append(a)
dataY.append(df[i + previous, 0])
return np.array(dataX), np.array(dataY)
scaler = sklearn.preprocessing.MinMaxScaler(feature_range = (0, 1))
df = scaler.fit_transform(df)
train_size = math.ceil(len(df) * 0.5)
train, val = df[0:train_size,:], df[train_size:len(df),:]
X_train, Y_train = create_dataset(train, previous)
print(X_train)
print(Y_train)
print(X_train.shape)
print(Y_train.shape)
X_val, Y_val = create_dataset(val, previous)
X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
X_val = np.reshape(X_val, (X_val.shape[0], 1, X_val.shape[1]))
model = keras.models.Sequential()
model.add(keras.layers.Dense(units = 64, activation = 'relu', input_shape = (1, 5)))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(units = 1, activation = 'linear'))
model.compile(loss='mean_absolute_error',
optimizer='adam',
metrics=['accuracy'])
history = model.fit(X_train, Y_train, epochs=8)
train = model.predict(X_train)
val = model.predict(X_val)
train = scaler.inverse_transform(train)
Y_train = scaler.inverse_transform([Y_train])
val = scaler.inverse_transform(val)
Y_val = scaler.inverse_transform([Y_val])
predictions = val
trainPlot = np.empty_like(df)
trainPlot[:, :] = np.nan
trainPlot[previous:len(train)+previous, :] = train
valPlot = np.empty_like(df)
valPlot[:, :] = np.nan
valPlot[len(train)+(previous*2)+1:len(df)-1, :] = val
inversetransform, =plt.plot(scaler.inverse_transform(df))
train, =plt.plot(trainPlot)
val, =plt.plot(valPlot)
plt.xlabel('Number of Days')
plt.ylabel('Stock Price')
plt.title("Predicted vs. Actual Stock Price Per Day")
plt.show()
func_stock_prediction("PLAY", 2010-1-1, 2020-1-1)

You are using accuracy as a metric. Accuracy measures the proportion of predicted labels that match the true labels. Accuracy is used mostly (to my knowledge) for classification tasks. As far as I know, the accuracy is not really interpretable when you're predicting a continuous outcome variable.
Based on your code, it looks like you're using the neural network for a regression problem (you're predicting a continuous variable). For regression problem meterics, people often use "mean squared error", "root mean squared error", "mean absolute error", "R^2", etc.
If you're interested in percentage differences, then maybe you could try the keras loss, "mean_absolute_percentage_error".

Keras accuracy does not change

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"))