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cnn train.py
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cnn train.py
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from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPool2D
from keras.optimizers import SGD
from keras.models import load_model
import matplotlib.pyplot as plt
(x_train, y_train), (x_test, y_test) = mnist.load_data()
plt.imshow(x_train[0])
# input image row and column
input_img_row = x_train[0].shape[0]
input_img_cols = x_train[0].shape[1]
x_train = x_train.reshape(x_train.shape[0], input_img_row, input_img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], input_img_row, input_img_cols, 1)
input_shape = (input_img_row, input_img_cols, 1)
x_train = x_train.astype("float32")
x_test = x_test.astype("float32")
# normalize the input data
x_train = x_train / 255
x_test = x_test / 255
# one hot encoder of the labels
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_train.shape[1]
num_pixels = x_train.shape[1] * x_train.shape[2]
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation="relu", input_shape=input_shape))
model.add(Conv2D(filters=64, kernel_size=(3, 3), activation="relu"))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(units=128, activation="relu", ))
model.add(Dropout(0.5))
model.add(Dense(units=num_classes, activation="softmax", ))
model.compile(optimizer=SGD(0.01), loss="categorical_crossentropy", metrics=["accuracy"])
model.summary()
train = model.fit(
x=x_train,
y=y_train,
batch_size=35,
epochs=10,
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])
model_file_path = input('Enter a file path (with .h5 extension) where the model will be saved:')
model.save(model_file_path)
loaded_model = load_model(model_file_path)
score = loaded_model.evaluate(x_test, y_test, verbose=0)
print("test loss", score[0])
print("accuracy", score[1])
print('Code is done, so everything works fine!')