Keras Model Examples for Deep Learning

Keras Functional API



In [1]:

    
# Multilayer Perceptron 10 inputs, 3 hidden layers with 10, 20, and 10 neurons, and an output layer with 1 output
from keras.utils import plot_model
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
visible = Input(shape=(10,))
hidden1 = Dense(10, activation='relu')(visible)
hidden2 = Dense(20, activation='relu')(hidden1)
hidden3 = Dense(10, activation='relu')(hidden2)
output = Dense(1, activation='sigmoid')(hidden3)
model = Model(inputs=visible, outputs=output)
# summarize layers
model.summary()
# plot graph
plot_model(model, to_file='multilayer_perceptron_graph.png')









    



Using TensorFlow backend.






    



_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 10)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_2 (Dense)              (None, 20)                220       
_________________________________________________________________
dense_3 (Dense)              (None, 10)                210       
_________________________________________________________________
dense_4 (Dense)              (None, 1)                 11        
=================================================================
Total params: 551.0
Trainable params: 551.0
Non-trainable params: 0.0
_________________________________________________________________



In [2]:

    
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
img=mpimg.imread('multilayer_perceptron_graph.png')
imgplot = plt.imshow(img)
plt.show()



In [3]:

    
# Convolutional Neural Network
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D
visible = Input(shape=(64,64,1))
conv1 = Conv2D(32, kernel_size=4, activation='relu')(visible)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(16, kernel_size=4, activation='relu')(pool1)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
hidden1 = Dense(10, activation='relu')(pool2)
output = Dense(1, activation='sigmoid')(hidden1)
model = Model(inputs=visible, outputs=output)
# summarize layers
model.summary()
# plot graph
plot_model(model, to_file='convolutional_neural_network.png')









    



_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         (None, 64, 64, 1)         0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 61, 61, 32)        544       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 30, 30, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 27, 27, 16)        8208      
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 13, 13, 16)        0         
_________________________________________________________________
dense_5 (Dense)              (None, 13, 13, 10)        170       
_________________________________________________________________
dense_6 (Dense)              (None, 13, 13, 1)         11        
=================================================================
Total params: 8,933.0
Trainable params: 8,933.0
Non-trainable params: 0.0
_________________________________________________________________



In [4]:

    
img=mpimg.imread('convolutional_neural_network.png')
imgplot = plt.imshow(img)
plt.show()



In [5]:

    
# Recurrent Neural Network
from keras.layers.recurrent import LSTM
visible = Input(shape=(100,1))
hidden1 = LSTM(10)(visible)
hidden2 = Dense(10, activation='relu')(hidden1)
output = Dense(1, activation='sigmoid')(hidden2)
model = Model(inputs=visible, outputs=output)
# summarize layers
model.summary()
# plot graph
plot_model(model, to_file='recurrent_neural_network.png')









    



_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         (None, 100, 1)            0         
_________________________________________________________________
lstm_1 (LSTM)                (None, 10)                480       
_________________________________________________________________
dense_7 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_8 (Dense)              (None, 1)                 11        
=================================================================
Total params: 601.0
Trainable params: 601.0
Non-trainable params: 0.0
_________________________________________________________________



In [6]:

    
img=mpimg.imread('recurrent_neural_network.png')
imgplot = plt.imshow(img)
plt.show()

Keras Sequential API



In [7]:

    
from keras.models import Sequential
from keras.layers import Activation
model = Sequential()
#model.add(Dense(5))
#layers = [Dense(5)]
#model = Sequential(layers)
model.add(Dense(5, input_dim=2))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(optimizer='sgd', loss='mean_squared_error', metrics=['accuracy'])
#algorithm = SGD(lr=0.1, momentum=0.3)
#model.compile(optimizer=algorithm, loss='mean_squared_error')

Keras fit, evaluate, predict



In [ ]:

    
history = model.fit(X, y, batch_size=10, epochs=100)

loss, accuracy = model.evaluate(X, y)

predictions = model.predict(X)

# Multi-Class
predictions = model.predict_classes(X)