visualization.py

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from keras import backend as K
from quiver_engine import server  # https://github.com/keplr-io/quiver
K.set_image_dim_ordering('th')


model = Sequential()
model.add(Convolution2D(32, kernel_size=(3, 3),padding='same',input_shape=(3 , 100, 100)))
model.add(Activation('relu'))
model.add(Convolution2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Convolution2D(64,(3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(5))
model.add(Activation('sigmoid'))


# let's train the model using SGD + momentum (how original).
sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=False)

model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.load_weights("weights.hdf5")

server.launch(model,input_folder='./',temp_folder='./filters')