Visualization

In [6]:

    

from keras.models import Sequential,load_model
from keras.layers.core import Dense, Activation,Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras import optimizers

model = Sequential()
        
model.add(Conv2D(filters=128,kernel_size=3,input_shape=(4,4,1)))
model.add(BatchNormalization())
model.add(Activation('relu'))

#for i in range(blocksize-1):
#    model.add(Conv2D(filters=64,kernel_size=3))
#    model.add(BatchNormalization())
#    model.add(Activation('relu'))
        
model.add(Conv2D(filters=128,kernel_size=2))
model.add(BatchNormalization())
model.add(Activation('relu'))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dense(4))
model.add(Activation('relu'))

adam = optimizers.Adam(lr=1e-6)
model.compile(loss='mse',optimizer=adam)
        
from keras.utils import plot_model
plot_model(model, to_file='alphago.png',show_shapes=True,show_layer_names=False)


    

In [2]:

    

from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot

SVG(model_to_dot(model).create(prog='dot', format='svg'))


    

    
Out[2]:

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