Image classification using simple neural network with keras − Blog by dchandra

This topic explains the image classification using simple neural network with Keras.

import numpy as np
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt
import random
import keras

from keras import models
from keras.layers import Dense, Dropout
from keras.utils import to_categorical
from keras.datasets import mnist

img_size = 28
n_class = 10
batch_size = 32
epochs = 10

# Load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train[:10000,:,:]
X_test = X_test[:1000,:,:]
y_train = y_train[:10000]
y_test  = y_test[:1000]

# Reshape data
X_train = X_train.reshape((X_train.shape[0], img_size * img_size))
X_train = X_train.astype('float32') / 255
X_test = X_test.reshape((X_test.shape[0], img_size * img_size))
X_test = X_test.astype('float32') / 255

# Categorically encode labels
y_train = to_categorical(y_train, n_class)
y_test = to_categorical(y_test, n_class)

# Build neural network
model = models.Sequential()
model.add(Dense(512, activation='relu', input_shape=(img_size*img_size,)))
model.add(Dropout(0.5))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(10, activation='softmax'))

# Compile model
opt= keras.optimizers.Adagrad(lr=0.001, epsilon=None, decay=0.0)
model.compile(optimizer=opt,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

model.fit(X_train, y_train,
          batch_size=batch_size,
          epochs=epochs,verbose = 1,
          validation_data=(X_test, y_test))

Train on 10000 samples, validate on 1000 samples
Epoch 1/10
10000/10000 [==============================] - 4s 382us/step - loss: 0.9976 - acc: 0.7107 - val_loss: 0.5790 - val_acc: 0.8380
Epoch 2/10
10000/10000 [==============================] - 3s 323us/step - loss: 0.5869 - acc: 0.8285 - val_loss: 0.4716 - val_acc: 0.8680
Epoch 3/10
10000/10000 [==============================] - 3s 295us/step - loss: 0.4939 - acc: 0.8561 - val_loss: 0.4218 - val_acc: 0.8810
Epoch 4/10
10000/10000 [==============================] - 3s 299us/step - loss: 0.4550 - acc: 0.8683 - val_loss: 0.3924 - val_acc: 0.8880
Epoch 5/10
10000/10000 [==============================] - 3s 291us/step - loss: 0.4244 - acc: 0.8747 - val_loss: 0.3755 - val_acc: 0.8890
Epoch 6/10
10000/10000 [==============================] - 3s 291us/step - loss: 0.4080 - acc: 0.8803 - val_loss: 0.3582 - val_acc: 0.8960
Epoch 7/10
10000/10000 [==============================] - 3s 321us/step - loss: 0.3839 - acc: 0.8891 - val_loss: 0.3448 - val_acc: 0.8970
Epoch 8/10
10000/10000 [==============================] - 3s 348us/step - loss: 0.3702 - acc: 0.8935 - val_loss: 0.3369 - val_acc: 0.9020
Epoch 9/10
10000/10000 [==============================] - 3s 331us/step - loss: 0.3488 - acc: 0.8988 - val_loss: 0.3278 - val_acc: 0.9020
Epoch 10/10
10000/10000 [==============================] - 3s 331us/step - loss: 0.3437 - acc: 0.8995 - val_loss: 0.3196 - val_acc: 0.9040

score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# Summary of neural network
model.summary()

Test loss: 0.31958454704284667
Test accuracy: 0.904
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 512)               401920    
_________________________________________________________________
dropout_1 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 256)               131328    
_________________________________________________________________
dropout_2 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 10)                2570      
=================================================================
Total params: 535,818
Trainable params: 535,818
Non-trainable params: 0
_________________________________________________________________

X_test = X_test.reshape((X_test.shape[0], img_size*img_size))

indices = random.sample(range(X_test.shape[0]),k=10)
X_test_samples = X_test[indices]
y_test_samples = y_test[indices]

for i in range(10):
  predict = model.predict_classes(X_test_samples[i].reshape((1,784)))
  actual = np.argmax(y_test_samples[i])
  plt.imshow(X_test_samples[i].reshape(28,28),cmap="binary")
  print("Actual Class :{}, Predicted Class: {}".format(actual,predict[0]))
  plt.show()