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from keras.layers import *
from keras.optimizers import Adam
from keras.losses import categorical_crossentropy
from keras.datasets import mnist
import numpy as np
(x_train, y_train), (x_test, y_test) = mnist.load_data()
def one_hot_encode(values, num_classes):
return np.eye(num_classes)[values]
y_train = one_hot_encode(y_train, 10)
y_test = one_hot_encode(y_test, 10)
x_train = np.reshape(x_train, (60000, 28 * 28))
x_test = np.reshape(x_test, (10000, 28 * 28))
model = Sequential()
model.add(Dense(32, input_shape=(28*28,)))
model.add(LeakyReLU(0.1))
model.add(Dense(10))
model.add(Softmax())
model.compile(optimizer=Adam(lr=0.001), \
loss=categorical_crossentropy, \
metrics=["accuracy"]
)
model.fit(x=x_train, y=y_train, batch_size=32, \
epochs=10, validation_data=(x_test, y_test))
from keras.layers import *
model = Sequential()
model.add(Dense(3, input_shape = (2,),use_bias =True))
model.add(Activation("tanh"))
model.add(Dense(1, use_bias = True))
model.add(Activation("sigmoid"))
model.summary()