from keras.models import Sequential
from keras.layers import Dense, Activation
 
model = Sequential([
Dense(32, input_dim=784),
Activation('relu'),
Dense(10),
Activation('softmax'),
])

model = Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))

from keras.layers import Input, Dense
from keras.models import Model
 
# this returns a tensor
inputs = Input(shape=(784,))
 
# a layer instance is callable on a tensor, and returns a tensor
x = Dense(64, activation='relu')(inputs)
x = Dense(64, activation='relu')(x)
predictions = Dense(10, activation='softmax')(x)
 
# this creates a model that includes
# the Input layer and three Dense layers
model = Model(input=inputs, output=predictions)
 
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
 
model.fit(data, labels) # starts training

model = torch.nn.Sequential(
 torch.nn.Linear(D_in, H),
 torch.nn.ReLU(),
 torch.nn.Linear(H, D_out),
)

layer1 = nn.Sequential()
layer1.add_module('conv1', nn.Conv2d(3, 32, 3, 1, padding=1))
layer1.add_module('relu1', nn.ReLU(True))
layer1.add_module('pool1', nn.MaxPool2d(2, 2))