# two ways of defining activation (both equal)

model = keras.Sequential([

keras.layers.Dense(1024, activation='relu', input_dim=64)),

keras.layers.Dense(256, activation='relu'),

keras.layers.Dense(10, activation='softmax'),

])

model = keras.Sequential([

keras.layers.Dense(1024, input_dim=64),

keras.layers.Activation(relu),

keras.layers.Dense(256),

keras.layers.Activation('relu'),

keras.layers.Dense(10),

keras.layers.Activation('softmax'),

])

# functional api

input_ = keras.layers.Input(shape=X_train.shape[1:])

hidden1 = keras.layers.Dense(30, activation='relu')(input_)

hidden2 = keras.layers.Dense(30, activation='relu')(hidden1)

concat = keras.layers.Concatenate()([input_, hidden2])

output = keras.layers.Dense(1)(concat)

model = keras.Model(inputs=[input_], outputs=[output])

# functional api - multiple inputs

input_A = keras.layers.Input(shape=[5], name='wide_input')

input_B = keras.layers.Input(shape=[6], name='deep_input')

hidden1 = keras.layers.Dense(30, activation='relu')(input_B)

hidden2 = keras.layers.Dense(30, activation='relu')(hidden1)

concat = keras.layers.concatenate([input_A, hidden2])

output = keras.layers.Dense(1, name='output')(concat)

model = keras.Model(inputs=[input_A, input_B], outputs=[output])

# get learning curves (metrics measured at end of each epoch)

import pandas as pd

import matplotlib.pyplot as plt

history = model.fit(...)

pd.DataFrame(history.history).plot(figsize=(8,5))

plt.grid(True)

plt.gca().set_ylim(0,1) # set the vertical range to [0-1]

plt.show()

# convert integer to binary array

from keras.utils import to_categorical

# https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical

to_categorical([0,1]) # [ [1,0], [0,1] ]

to_categorical([0,2]) # [ [1,0,0], [0,0,1] ]

to_categorical([0,1], 4) # [ [1,0,0,0], [0,1,0,0] ]

to_categorical([1,4], 3) # err