I am currently working on handwritten digit recognition of regional languages. Currently, I am focusing on Oriya. I test the MNIST dataset through the CNN model and I am trying to apply the model on my Oriya dataset. Model is performing poorly. It is giving the wrong predictions. I have a dataset of 4971 samples.
How to improve the accuracy?
Here's my code:
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD,RMSprop,adam
from keras.utils import np_utils
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import os
import theano
from PIL import Image
from numpy import *
# SKLEARN
from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# input image dimensions
img_rows, img_cols = 28, 28
# number of channels
img_channels = 1
path2 = '/home/saumya/Desktop/Oriya/p' #path of folder of images
imlist = os.listdir(path2)
im1 = array(Image.open('/home/saumya/Desktop/Oriya/p' + '/'+ imlist[0])) # open one image to get size
m,n = im1.shape[0:2] # get the size of the images
imnbr = len(imlist) # get the number of images
# create matrix to store all flattened images
immatrix = array([array(Image.open('/home/saumya/Desktop/Oriya/p' + '/'+ im2)).flatten()
for im2 in imlist],'f')
label=np.ones((num_samples,),dtype = int)
label[1:503]=0
label[503:1000]=1
label[1000:1497]=2
label[1497:1995]=3
label[1995:2493]=4
label[2493:2983]=5
label[2983:3483]=6
label[3483:3981]=7
label[3981:4479]=8
label[4479:4972]=9
print(label[1000])
data,Label = shuffle(immatrix,label, random_state=2)
train_data = [data,Label]
img=immatrix[2496].reshape(img_rows,img_cols)
plt.imshow(img)
plt.show()
(X, y) = (train_data[0],train_data[1])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
def baseline_model():
# create model
model = Sequential()
model.add(Conv2D(32, (3,3), input_shape=(1, 28, 28), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
#model.add(Conv2D(64, (5, 5), input_shape=(1, 10, 10), activation='relu'))
#model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax', name = 'first_dense_layer'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
# build the model
model = baseline_model()
# Fit the model
hist=model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=30, batch_size=100, verbose=2)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("CNN Error: %.2f%%" % (100-scores[1]*100))
score = model.evaluate(X_test, y_test, verbose=0)
print('Test Loss:', score[0])
print('Test accuracy:', score[1])
test_image = X_test[0:1]
print (test_image.shape)
print(model.predict(test_image))
print(model.predict_classes(test_image))
print(y_test[0:1])
# define the larger model
def larger_model():
# create model
model = Sequential()
model.add(Conv2D(30, (5, 5), input_shape=(1, 28, 28), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(15, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(50, activation='relu', name='first_dense_layer'))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
# build the model
model = larger_model()
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Large CNN Error: %.2f%%" % (100-scores[1]*100))
I am trying to resize my model using opencv, it is generating the following error:
/root/mc-x64-2.7/conda-bld/opencv-3_1482254119970/work/opencv-3.1.0/modules/imgproc/src/imgwarp.cpp:3229: error: (-215) ssize.area() > 0 in function resize
How to improve the accuracy?
A bit hard to give a detailed answer from what you posted, and without seeing some data sample, but still will attempt a stab at this. What I can see that may help improve you accuracy is:
Get more data. In Deep Learning one usually works with big amount of data, and models almost always improve when adding more data. If you can't obtain new data you may try to generate more samples with the ones you got, by adding noise or similar modifications.
I see you currently have 30 and 10 epochs on the training of your model. I suggest you increase the number of epochs, so your model has more time to converge. This also most of the times improves performance up to a point.
I also see that your batch size is 100 and 200 on your models. You can try reducing the batch size of your training process, so your training performs gradient update more times on each epoch (remember that you can even use batch_size=1 to upgrade your model for each sample, instead of batches).
Alternatively, you can try iteratively increasing the complexity and layers of your architecture and compare your performances. It is best to start with a simple model, train and test, and then add layers and nodes until you are satisfied with the results. I also see you have tried a hybrid convolutional and non-convolutional approach; you can well try starting with just one of the approaches before increasing the complexity of your architecture.
Related
I trained the CNN on MNIST dataset with training and validation accuracy of ~0.99.
I followed the exact steps from the example given at the Keras documentation of implementing CNN with MNIST dataset:
import cv2
import numpy as np
import tensorflow.keras as keras
import math
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
batch_size = 128
num_classes = 10
epochs = 12
# input image dimensions
img_rows, img_cols = 28, 28
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
When I tested the following image:
using the following test code:
img = cv2.imread("m9.png", 0)
img = cv2.resize(img, (28,28))
img = img / 255.
prob = model.predict_proba(img.reshape((1,28, 28, 1)))
print(prob)
model.predict_classes(img.reshape((1,28, 28, 1)))
The class it prints out is array([1]) , denoting number 1. I could not understand the reason for it. Did I try to predict in an incorrect way?
Exactly same class array([1]) was predicted for number 8 as shown below:
It looks like I have made an error during prediction? I tried to understand what could be happening but could not understand.
There is no error, its just that your images don't look at all like the ones in the MNIST dataset. This dataset is not meant to train a general digit recognition algorithm, it will only work with similar images.
In your case the digits will be very small in a 28x28 image, so the predictions are kind of random.
You are resizing the input image to 28 X 28. Instead you should first crop the image around the digit to make it look like the data-set in MNIST. Otherwise in resized image, the digit will occupy very small portion and results will be arbitrary.
I am a total beginner in keras i implemented following code in keras, i found this code on web and successfully trained it with 97 % accuracy. I am getting little bit problem during Prediction.
The following code for training:
from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, Adam
from keras.utils import np_utils
import numpy as np
#seed = 7
#np.random.seed(seed)
batch_size = 50
nb_classes = 10
nb_epoch = 150
data_augmentation = False
# input image dimensions
img_rows, img_cols = 32, 32
# the CIFAR10 images are RGB
img_channels = 3
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='same',
input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
# let's train the model using SGD + momentum (how original).
#sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
sgd= Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
if not data_augmentation:
print('Not using data augmentation.')
model.fit(X_train, Y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
validation_data=(X_test, Y_test),
shuffle=True)
else:
print('Using real-time data augmentation.')
# this will do preprocessing and realtime data augmentation
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
datagen.fit(X_train)
# fit the model on the batches generated by datagen.flow()
model.fit_generator(datagen.flow(X_train, Y_train,
batch_size=batch_size),
samples_per_epoch=X_train.shape[0],
nb_epoch=nb_epoch,
validation_data=(X_test, Y_test))
model.save('model3.h5')
The model was saved successfully and i implemented this following Prediction code.
Code for Prediction:
import keras
import tensorflow as tf
import h5py
from keras.models import load_model
import cv2
import numpy as np
model = load_model('model3.h5')
print('Model Loaded')
dim = (32,32)
img = cv2.imread('download.jpg')
img = cv2.resize(img,dim)
Array = [np.array(img)]
Prediction = model.predict(Array)
print(Prediction)
Error generated:
Using TensorFlow backend.
Model Loaded
Traceback (most recent call last):
File "E:\Prediction\Prediction.py", line 16, in <module>
Prediction = model.predict(Array)
File "C:\Users\Dilip\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training.py", line 1149, in predict
x, _, _ = self._standardize_user_data(x)
File "C:\Users\Dilip\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training.py", line 751, in _standardize_user_data
exception_prefix='input')
File "C:\Users\Dilip\AppData\Local\Programs\Python\Python36\lib\site-packages\keras\engine\training_utils.py", line 128, in standardize_input_data
'with shape ' + str(data_shape))
ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (32, 32, 3)
>>>
I know here that some problem is generated for not being in a proper shape of the input image i tried to convert it into (1,32,32,3) but i failed !!
Help here please.
It appears you are missing the classes in your code for prediction. Try this instead:
import cv2
import tensorflow as tf
#write the 10 classes here nb_classes
CATEGORIES = ['1','2','3','4','5','6','7','8','9','10']
def prepare(filepath):
IMG_SIZE = 32
img_array = cv2.imread(filepath, cv2.IMREAD_COLOR)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 3) #img_channels = 3
model = tf.keras.models.load_model('model3.h5')
prediction = model.predict([prepare('download.jpg')])
print(CATEGORIES[int(prediction[0][0])])
I use the following code (courtesy to here) which runs CNN for training MNIST images:
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
batch_size = 128
num_classes = 10
epochs = 1
# input image dimensions
img_rows, img_cols = 28, 28
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
print(model.save_weights('file.txt')) # <<<<<----
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
My goal is to use CNN model to extract MNIST features into a dataset that I can use as an input for another classifier. In this example, I don't care about the classification operation since all I need is the features of the trained images. The only method I found is save_weights as:
print(model.save_weights('file.txt'))
How can I extract features into a dataset from keras model?
After training or loading the existing trained model, you can create another model:
extract = Model(model.inputs, model.layers[-3].output) # Dense(128,...)
features = extract.predict(data)
and use the .predict method to return the vectors from a specific layer, in this case every image will become (128,), the output of the Dense(128, ...) layer.
You can also train these networks jointly with 2 outputs using the functional API. Follow the guide and you'll see that you can chain models together and have multiple outputs each possibly with a separate loss. This will allow your model to learn shared features that is useful for both classifying the MNIST image and your task at the same time.
I am making a simple application to implement digit recognition. The problem is that it works wonderfully on the mnist dataset but predicts horribly on random images downloaded from google. What should I do to improve the accuracy of my model?
Another question : Someone suggested to add more layers in the model. If so, how do I add more layers to my model?
File in which model is trained:
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import cv2
import matplotlib.pyplot as plt
from keras.models import load_model
import pickle
import h5py
import numpy as np
from keras.callbacks import ModelCheckpoint
batch_size = 128
num_classes = 10
epochs = 12
# input image dimensions
img_rows, img_cols = 28, 28
model=load_model('my_model.h5')
the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
print('initial shape')
print('x_test ',x_test.shape)
print('y_test ',y_test.shape)
# print(x_test);
# print(y_test.shape)
if K.image_data_format() == 'channels_first':
print('reshape1')
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
print('reshape2')
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print('x_test shape: ' , x_test.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print('x_test final : ')
print(x_test)
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.10))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.20))
model.add(Dense(256,activation='relu'))
model.add(Dropout(0.40))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
model.save('my_model.h5')
score = model.evaluate(x_test, y_test, verbose=0)
# print(x_test.shape)
# print('\n')
# print(y_test.shape)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
The below code is used for testing my model with my own image
image = cv2.imread("2.jpg")
img_rows, img_cols = 28, 28
x_test1 = cv2.resize(image, (28, 28))
x_test1 = cv2.cvtColor(x_test1,cv2.COLOR_RGB2GRAY)
print(x_test1.shape)
if K.image_data_format() == 'channels_first':
print('reshape1')
x_test1 = x_test1.reshape(1, 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
print('reshape2')
x_test1 = x_test1.reshape(1, img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_test1 = x_test1.astype('float32')
x_test1 /= 255
y_test1 = np.array([[ 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.]])
score = model.evaluate(x_test1, y_test1, verbose=0)
print('done')
print('score of image = ')
print(score[1])
print(score[0])
score=model.predict_classes(x_test1)
print(score)
The below code is used to load a previous trained model and continue training it from the previous checkpoint. If there are any mistakes please do suggest them.
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
filepath="my_model.h5"
checkpoint = ModelCheckpoint(filepath, monitor='acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
score=model.fit(x_test1, y_test1,epochs=12, batch_size=128, callbacks=callbacks_list, verbose=0)
new_model = load_model("my_model.h5")
np.testing.assert_allclose(model.predict(x_test1),
new_model.predict(x_test1),
1e-5)
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]
new_model.fit(x_train, y_train, epochs=12, batch_size=128, callbacks=callbacks_list)
Does the above code cause over-fitting of the model ? If yes how do I make it more efficient so that it is able to predict any kind of data ? Help Required!!
You should not be surprised by these results because you train your model on one data domain but test it on a different one. Yes, you may get slightly better performance if you have a better-designed network, but this gap still exists.
To close this gap, you can improve the following things :
improve your training data: (a) train your model with more real-looking digit data, for example, the street view house number (SVHN) dataset, or the digits in the char74K dataset, and (b) train your model with better data augmentation techniques, for example, blend a digit sample with a random background image.
adapt your testing data: preprocess testing samples such that they look similar to those in your training data, for example, binzarize a testing sample and let it look similar to that of MNIST, before you feed it to your network for prediction.
improve your model by explicitly considering the variations in real data: for example, if you think it is rotation in testing data degrades your model performance, then you can consider this factor in your model training by adding a side task to prediction digit rotation; or you believe a good model should only predict digits but not the dataset that this training sample belongs to, then you can add an adversarial task to force the network forget this information.
I am trying out the mnist tutorial using keras at https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py but am getting really low accuracy. I got a test accuracy of 83% on my 1st trial after 12 epochs and 75% on my 2nd trial. The page claims that it is able to get 99.25% test accuracy after 12 epochs. I am not sure why this is happening because I literally copy pasted the code into PyCharm.
I am using Keras version 2.0.0 with Tensorflow version 0.12.1 as its backend. Training is done via CPU.
Could the reason be due to the version? Should I upgrade to the latest versions of Keras and Tensorflow? I hope someone can run the exact code and let me know of their results after 12 epochs with the Keras and Tensorflow version.
Or could the reason be due to the variability in deep learning and that I might eventually get a ~99% test accuracy after 12 epochs if I run enough simulations. This is the code in case anyone doesn't wanna get into the link
'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
batch_size = 128
num_classes = 10
epochs = 12
# input image dimensions
img_rows, img_cols = 28, 28
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])