nmist_prediction.py

import keras
import pdb
from keras.datasets import mnist 
from keras.models import Sequential 
from keras.layers import Dense, Dropout 
from keras.optimizers import RMSprop 
import numpy as np

# Load mnist data
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Process the data
x_train = x_train.reshape(60000, 784) # reshape the input from (28, 28) tuple to (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 

y_train = keras.utils.to_categorical(y_train, 10)  # is used to convert vector to binary matrix
y_test = keras.utils.to_categorical(y_test, 10)

# Create the model
model = Sequential()
model.add(Dense(512, activation = 'relu', input_shape = (784,)))
model.add(Dropout(0.2)) 
model.add(Dense(512, activation = 'relu'))
model.add(Dropout(0.2)) 
model.add(Dense(10, activation = 'softmax'))

# Compile the model
model.compile(loss = 'categorical_crossentropy',
              optimizer = RMSprop(), 
              metrics = ['accuracy'])

# Train the model
history = model.fit(
   x_train, y_train, 
   batch_size = 128, 
   epochs = 20, 
   verbose = 1, 
   validation_data = (x_test, y_test)
)

# evaluate the model
score = model.evaluate(x_test, y_test, verbose = 0) 

# let's predict now
pred = model.predict(x_test) 
pred = np.argmax(pred, axis = 1)[:5] 
label = np.argmax(y_test,axis = 1)[:5] 

print(pred) # outputs: [7 2 1 0 4] 
print(label) # outputs: [7 2 1 0 4]