Keras Neural Network With Custom Input - python

What I'm trying to accomplish is having a set relationship between some custom input data and output data then have a neural network figure out this relationship/rule to predict future output given the input. I've set up some test code here where a random list of inputs is generated and if it is more than 0.5 the output is 1 otherwise the output is 0.
from tensorflow import keras
import numpy as np
# generate data
data_input_generate = np.random.random((6400, 1))
data_output_generate = np.random.randint(2, size=(6400, 1))
data_input = np.vstack([data_input_generate, data_input_generate])
data_output = np.vstack([data_output_generate, data_output_generate])
for i in range(len(data_input)):
if data_input[i] >= 0.5:
data_output[i] = [1]
else:
data_output[i] = [0]
# setup neural network
Inputs = keras.layers.Input(shape=(1, ))
hidden1 = keras.layers.Dense(units=100, activation="sigmoid")(Inputs)
hidden2 = keras.layers.Dense(units=100, activation='softmax')(hidden1)
predictions = keras.layers.Dense(units=1, activation='relu')(hidden2)
# initialize model
model = keras.Model([Inputs], outputs=predictions)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit
model.fit(data_input, data_output, batch_size=10, epochs=5)
# predict
predictions = model.predict(data_input_generate)
# print predictions
for i in range(10):
print(f"Value: {data_input_generate[i]}, Result: {data_output_generate[i]}, Prediction: {predictions[i]}")
The problem is, after fitting the model, the accuracy stays at 50%. Is this a problem with my layer activation function or the way I set up the model? My goal is to correctly predict the output with fairly high accuracy. Thanks in advance!

Try using a sigmoid activation function on your output layer. Here is a working example:
from tensorflow import keras
import numpy as np
# generate data
data_input_generate = np.random.random((6400, 1))
data_output_generate = np.random.randint(2, size=(6400, 1))
data_input = np.vstack([data_input_generate, data_input_generate])
data_output = np.vstack([data_output_generate, data_output_generate])
for i in range(len(data_input)):
if data_input[i] >= 0.5:
data_output[i] = [1]
else:
data_output[i] = [0]
# setup neural network
Inputs = keras.layers.Input(shape=(1, ))
hidden1 = keras.layers.Dense(units=64, activation="relu")(Inputs)
hidden2 = keras.layers.Dense(units=32, activation='relu')(hidden1)
dropout = keras.layers.Dropout(0.8)(hidden2)
predictions = keras.layers.Dense(units=1, activation='sigmoid')(dropout)
# initialize model
model = keras.Model([Inputs], outputs=predictions)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit
model.fit(data_input, data_output, batch_size=10, epochs=5)
# predict
predictions = model.predict(data_input_generate)
# print predictions
for i in range(10):
print(f"Value: {data_input_generate[i]}, Result: {data_output_generate[i]}, Prediction: {predictions[i]}")
Epoch 1/5
1280/1280 [==============================] - 4s 3ms/step - loss: 0.3632 - accuracy: 0.8327
Epoch 2/5
1280/1280 [==============================] - 3s 3ms/step - loss: 0.1870 - accuracy: 0.9427
Epoch 3/5
1280/1280 [==============================] - 3s 3ms/step - loss: 0.1528 - accuracy: 0.9475
Epoch 4/5
1280/1280 [==============================] - 3s 2ms/step - loss: 0.1461 - accuracy: 0.9482
Epoch 5/5
1280/1280 [==============================] - 2s 2ms/step - loss: 0.1384 - accuracy: 0.9493
Value: [0.79415764], Result: [0], Prediction: [0.9997529]
Value: [0.38311113], Result: [1], Prediction: [1.7478478e-05]
Value: [0.05360975], Result: [0], Prediction: [2.3240638e-07]
Value: [0.78635261], Result: [1], Prediction: [0.99970365]
Value: [0.74414175], Result: [1], Prediction: [0.99921006]
Value: [0.47845171], Result: [1], Prediction: [0.07256863]
Value: [0.53008247], Result: [0], Prediction: [0.886382]
Value: [0.40377478], Result: [1], Prediction: [9.9769844e-05]
Value: [0.18209166], Result: [1], Prediction: [5.199377e-07]
Value: [0.00937745], Result: [1], Prediction: [1.7613968e-07]

Related

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".

Batch Input Keras Shared Parameters

I am building network to rank a set of N inputs. Ideally they should all be input at the same time and share parameters. Their target vector should be an N-hot vector to match the inputs.
This means my input should be (Batch_size, N, sequence_length, feature_length)
But keras will throw an error for any input larger than 3 dimensions as shown here:
ValueError: Input 0 is incompatible with layer lstm_2: expected
ndim=3, found ndim=4
My current keras set up is:
x = Input(shape=(72,300))
aux_input = Input(shape=(72, 4))
probs = Input(shape=(1,))
#dim_red_1 = Dense(100)(x)
dim_red_2 = Dense(20, activation='tanh')(x)
cat = concatenate([dim_red_2, aux_input])
encoded = LSTM(64)(cat)
cat2 = concatenate([encoded, probs])
output = Dense(1, activation='sigmoid')(cat2)
lstm_model = Model(inputs=[x, aux_input, probs], outputs=output)
lstm_model.compile(optimizer='ADAM', loss='binary_crossentropy', metrics=['accuracy'])
Is there a way to achieve this with Keras?
Although your code seems to be fine, make sure to import the right packages:
import numpy as np
from tensorflow.python.keras import Input
from tensorflow.python.keras.engine.training import Model
from tensorflow.python.keras.layers import Dense, LSTM, Concatenate
a = np.zeros(shape=[1000, 72, 300])
b = np.zeros(shape=[1000, 72, 4])
c = np.zeros(shape=[1000, 1])
d = np.zeros(shape=[1000, 1])
x = Input(shape=(72, 300))
aux_input = Input(shape=(72, 4))
probs = Input(shape=(1,))
dim_red_2 = Dense(20, activation='tanh')(x)
cat = Concatenate()([dim_red_2, aux_input])
encoded = LSTM(64)(cat)
cat2 = Concatenate()([encoded, probs])
output = Dense(1, activation='sigmoid')(cat2)
lstm_model = Model(inputs=[x, aux_input, probs], outputs=output)
lstm_model.compile(optimizer='ADAM', loss='binary_crossentropy', metrics=['accuracy'])
lstm_model.summary()
lstm_model.fit([a, b, c], d, batch_size=256)
output:
256/1000 [======>.......................] - ETA: 2s - loss: 0.6931 - acc: 1.0000
512/1000 [==============>...............] - ETA: 1s - loss: 0.6910 - acc: 1.0000
768/1000 [======================>.......] - ETA: 0s - loss: 0.6885 - acc: 1.0000
1000/1000 [==============================] - 1s 1ms/step - loss: 0.6859 - acc: 1.00

Keras LSTM + TensorFlow and a number sequence (improve loss)

first of all, I'm running with the following setup:
Running on windows 10
Python 3.6.2
TensorFlow 1.8.0
Keras 2.1.6
I'm trying to predict, or at least guesstimate the following number sequence:
https://codepen.io/anon/pen/RJRPPx (limited to 20,000 for testing), the full sequence contains about one million records.
And here is the code (run.py)
import lstm
import time
import matplotlib.pyplot as plt
def plot_results(predicted_data, true_data):
fig = plt.figure(facecolor='white')
ax = fig.add_subplot(111)
ax.plot(true_data, label='True Data')
plt.plot(predicted_data, label='Prediction')
plt.legend()
plt.show()
def plot_results_multiple(predicted_data, true_data, prediction_len):
fig = plt.figure(facecolor='white')
ax = fig.add_subplot(111)
ax.plot(true_data, label='True Data')
#Pad the list of predictions to shift it in the graph to it's correct start
for i, data in enumerate(predicted_data):
padding = [None for p in range(i * prediction_len)]
plt.plot(padding + data, label='Prediction')
plt.legend()
plt.show()
#Main Run Thread
if __name__=='__main__':
global_start_time = time.time()
epochs = 10
seq_len = 50
print('> Loading data... ')
X_train, y_train, X_test, y_test = lstm.load_data('dice_amplified/primeros_20_mil.csv', seq_len, True)
print('> Data Loaded. Compiling...')
model = lstm.build_model([1, 50, 100, 1])
model.fit(
X_train,
y_train,
batch_size = 512,
nb_epoch=epochs,
validation_split=0.05)
predictions = lstm.predict_sequences_multiple(model, X_test, seq_len, 50)
#predicted = lstm.predict_sequence_full(model, X_test, seq_len)
#predicted = lstm.predict_point_by_point(model, X_test)
print('Training duration (s) : ', time.time() - global_start_time)
plot_results_multiple(predictions, y_test, 50)
I have tried to:
increase and decrease epochs.
increase and decrease batch size.
amplify the data.
The following plot represents:
epochs = 10
batch_size = 512
validation_split = 0.05
As well, as far as I understand, the loss should be decreasing with more epochs? Which doesn't seem to be happening!
Using TensorFlow backend.
> Loading data...
> Data Loaded. Compiling...
> Compilation Time : 0.03000473976135254
Train on 17056 samples, validate on 898 samples
Epoch 1/10
17056/17056 [==============================] - 31s 2ms/step - loss: 29927.0164 - val_loss: 289.8873
Epoch 2/10
17056/17056 [==============================] - 29s 2ms/step - loss: 29920.3513 - val_loss: 290.1069
Epoch 3/10
17056/17056 [==============================] - 29s 2ms/step - loss: 29920.4602 - val_loss: 292.7868
Epoch 4/10
17056/17056 [==============================] - 27s 2ms/step - loss: 29915.0955 - val_loss: 286.7317
Epoch 5/10
17056/17056 [==============================] - 26s 2ms/step - loss: 29913.6961 - val_loss: 298.7889
Epoch 6/10
17056/17056 [==============================] - 26s 2ms/step - loss: 29920.2068 - val_loss: 287.5138
Epoch 7/10
17056/17056 [==============================] - 28s 2ms/step - loss: 29914.0650 - val_loss: 295.2230
Epoch 8/10
17056/17056 [==============================] - 25s 1ms/step - loss: 29912.8860 - val_loss: 295.0592
Epoch 9/10
17056/17056 [==============================] - 28s 2ms/step - loss: 29907.4067 - val_loss: 286.9338
Epoch 10/10
17056/17056 [==============================] - 46s 3ms/step - loss: 29914.6869 - val_loss: 289.3236
Any recommendations? How could I improve it? Thanks!
Lstm.py contents:
import os
import time
import warnings
import numpy as np
from numpy import newaxis
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' #Hide messy TensorFlow warnings
warnings.filterwarnings("ignore") #Hide messy Numpy warnings
def load_data(filename, seq_len, normalise_window):
f = open(filename, 'rb').read()
data = f.decode().split('\n')
sequence_length = seq_len + 1
result = []
for index in range(len(data) - sequence_length):
result.append(data[index: index + sequence_length])
if normalise_window:
result = normalise_windows(result)
result = np.array(result)
row = round(0.9 * result.shape[0])
train = result[:int(row), :]
np.random.shuffle(train)
x_train = train[:, :-1]
y_train = train[:, -1]
x_test = result[int(row):, :-1]
y_test = result[int(row):, -1]
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
return [x_train, y_train, x_test, y_test]
def normalise_windows(window_data):
normalised_data = []
for window in window_data:
normalised_window = [((float(p) / float(window[0])) - 1) for p in window]
normalised_data.append(normalised_window)
return normalised_data
def build_model(layers):
model = Sequential()
model.add(LSTM(
input_shape=(layers[1], layers[0]),
output_dim=layers[1],
return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(
layers[2],
return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(
output_dim=layers[3]))
model.add(Activation("linear"))
start = time.time()
model.compile(loss="mse", optimizer="rmsprop")
print("> Compilation Time : ", time.time() - start)
return model
def predict_point_by_point(model, data):
#Predict each timestep given the last sequence of true data, in effect only predicting 1 step ahead each time
predicted = model.predict(data)
predicted = np.reshape(predicted, (predicted.size,))
return predicted
def predict_sequence_full(model, data, window_size):
#Shift the window by 1 new prediction each time, re-run predictions on new window
curr_frame = data[0]
predicted = []
for i in range(len(data)):
predicted.append(model.predict(curr_frame[newaxis,:,:])[0,0])
curr_frame = curr_frame[1:]
curr_frame = np.insert(curr_frame, [window_size-1], predicted[-1], axis=0)
return predicted
def predict_sequences_multiple(model, data, window_size, prediction_len):
#Predict sequence of 50 steps before shifting prediction run forward by 50 steps
prediction_seqs = []
for i in range(int(len(data)/prediction_len)):
curr_frame = data[i*prediction_len]
predicted = []
for j in range(prediction_len):
predicted.append(model.predict(curr_frame[newaxis,:,:])[0,0])
curr_frame = curr_frame[1:]
curr_frame = np.insert(curr_frame, [window_size-1], predicted[-1], axis=0)
prediction_seqs.append(predicted)
return prediction_seqs
Addendum:
At nuric suggestion I modified the model as following:
def build_model(layers):
model = Sequential()
model.add(LSTM(input_shape=(layers[1], layers[0]), output_dim=layers[1], return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(layers[2], return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(output_dim=layers[3]))
model.add(Activation("linear"))
model.add(Dense(64, input_dim=50, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
start = time.time()
model.compile(loss="mse", optimizer="rmsprop")
print("> Compilation Time : ", time.time() - start)
return model
Still a bit lost on this one...
Even though you normalise the input, you don't normalise the output. The LSTM by default has a tanh output which means you will have a limited feature space, ie the dense layer won't be able to regress to large numbers.
You have a fixed length numerical input (50,), directly pass that to Dense layers with relu activation and will perform better on regression tasks, something simple like:
model = Sequential()
model.add(Dense(64, input_dim=50, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
For regression it is also preferable to use l2 regularizers instead of Dropout because you are not really feature extracting for classification etc.

MobileNet transfer learning in Keras for object localization extraction - loss computed as NaN

I'm trying to use Keras and its MobileNet implementation to do object localization (output the x/y coordinates of a few features, instead of classes) and I'm running into some likely very basic issue that I can't figure out.
My code looks like this:
# =============================
# Load MobileNet and change the top layers.
model = applications.MobileNet(weights="imagenet",
include_top=False,
input_shape=(224, 224, 3))
# Freeze all the layers except the very last 5.
for layer in model.layers[:-5]:
layer.trainable = False
# Adding custom Layers at the end, after the last Conv2D layer.
x = model.output
x = GlobalAveragePooling2D()(x)
x = Reshape((1, 1, 1024))(x)
x = Dropout(0.5)(x)
x = Conv2D(1024, (1, 1), activation='relu', padding='same', name='conv_preds')(x)
x = Dense(1024, activation="relu")(x)
# I'd like this to output 4 variables, two pairs of x/y coordinates
x = Dense(PREDICT_SIZE, activation="sigmoid")(x)
predictions = Reshape((PREDICT_SIZE,))(x)
# =============================
# Create the new final model.
model_final = Model(input = model.input, output = predictions)
def custom_loss(y_true, y_pred):
'''Trying to compute the Euclidian distance as a Loss Function'''
return K.sqrt(K.sum(K.square(y_true - y_pred), axis=-1))
model_final.compile(loss = custom_loss,
optimizer = optimizers.adam(lr=0.0001),
metrics=["accuracy"])
With this model, then I load the data and try to train it.
x_train, y_train, x_val, y_val = load_data(DATASET_DIR)
# This load_data is my own implementation. It returns the images
# as tensors.
# ==> x_train[0].shape= (224, 224, 3)
#
# y_train and y_val look like this:
# ==> y_train[0]= [ 0.182 -0.0933 0.072 -0.0453]
#
# holding values in the [0, 1] interval for where the pixel
# is relative to the width/height of the image.
#
model_final.fit(x_train, y_train,
batch_size=batch_size, epochs=5, shuffle=False,
validation_data=(x_val, y_val))
Unfortunately, what I get when I run this model to train, I get something like this:
Train on 45 samples, validate on 5 samples
Epoch 1/5
16/45 [=========>....................] - ETA: 2s - loss: nan - acc: 0.0625
32/45 [====================>.........] - ETA: 1s - loss: nan - acc: 0.0312
45/45 [==============================] - 4s - loss: nan - acc: 0.0222 - val_loss: nan - val_acc: 0.0000e+00
Epoch 2/5
16/45 [=========>....................] - ETA: 2s - loss: nan - acc: 0.0625
32/45 [====================>.........] - ETA: 1s - loss: nan - acc: 0.0312
45/45 [==============================] - 4s - loss: nan - acc: 0.0222 - val_loss: nan - val_acc: 0.0000e+00
Epoch 3/5
I'm at a loss about why my loss value is "nan". I must be doing something wrong, and I've tried to change everything - the loss function, the shape of the output... but I can't figure out what I'm doing wrong.
Any help would be appreciated!
UPDATE: it seems like the issue is in the way I load_data.
If I create the image data like this it fails and results in loss:nan
i = pil_image.open(img_filename)
img = image.load_img(img_filename, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = keras.applications.mobilenet.preprocess_input(x)
x_train = np.append(x_train, x, axis=0)
but if I do something trivial like this, 'fit' works just fine and computes real values for loss:
x_train = np.random.random((100, 224, 224, 3))
sigh I wonder what's happening...
UPDATE #2: I figured out what the issue was
Documenting this here in case it helps anybody.
The way to properly generate the input tensors for MobileNet is this one:
test_img=[]
for i in range(len(test)):
temp_img=image.load_img(test_path+test['filename'][i],target_size=(224,224))
temp_img=image.img_to_array(temp_img)
test_img.append(temp_img)
test_img=np.array(test_img)
test_img=preprocess_input(test_img)
Notice how making it into a numpy.array and running preprocess_input happens on the whole batch of images. Doing it image by image seems to not have worked (what I was doing before).
Hope this helps somebody someday.

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"))

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