from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras import backend as K
batch_size = 64
num_classes = 10
epochs = 12
img_rows, img_cols = 28, 28
(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')
model = Sequential([
Flatten(),
Dense(512, activation='relu'),
Dense(512, activation='relu'),
Dropout(0.2),
Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size)
model.evaluate(x_test, y_test)
model1 = Sequential()
model1.add(Conv2D(6, (3, 3), padding='same', activation='relu',input_shape=input_shape))
model1.add(MaxPooling2D(pool_size=(2, 2)))
model1.add(Dropout(0.25))
model1.add(Flatten())
model1.add(Dense(100, activation='relu'))
model1.add(Dense(10, activation='softmax'))
model1.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model1.fit(x_train, y_train, epochs=10)
model1.evaluate(x_test, y_test)
from sklearn.ensemble import RandomForestClassifier
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
model.rf = RandomForestClassifier(n_estimators=500)
model.rf.fit(x_train, y_train)
print(model.rf.score(x_test,y_test))