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elowy01/nmist_prediction.py

Created August 27, 2022 17:34
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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]
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