Keras Deep Learning Cookbook

How to do it...

Create a sequential model with the appropriate network topology:

• Input layer with shape (*, 784), and an output of (*, 512)
• Hidden layer with an input (*, 512) and an output of (*, 512)
• Output layer with the input dimension as (*, 512) and the output as (*, 10)

Let's look at the activation functions for each layer:

• Layer 1 and Layer 1-relu
• Layer 3-softmax

from keras.optimizers import SGDy_train = keras.utils.to_categorical(y_train, num_classes)y_test = keras.utils.to_categorical(y_test, num_classes)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(num_classes, activation='softmax'))model.summary() ...