def receive_data(childPipe):
context = zmq.Context()
sub_recv = context.socket(zmq.SUB)
sub_recv.connect("tcp://" + IP + ":5557")
sub_recv.setsockopt(zmq.SUBSCRIBE, b'')
while True:
b_data_array = sub_recv.recv_pyobj()
childPipe.send(b_data_array)
#print("send to pipe")
continue
global model
model = MobileNet(weights=None, input_shape=(32, 32, 3), classes=10)
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
def get_gradient_func(model):
grads = K.gradients(model.total_loss, model.trainable_weights)
inputs = model._feed_inputs + model._feed_targets + model._feed_sample_weights
func = K.function(inputs, grads)
return func
def trainHandler(event, context):
worker_ids = event["worker_ids"] # the allocate work id
bat_size = event["batch_size"]
max_fnum = event["fuction_number"]
snumber = event["sub_data_size"]
# ImageNet weights, basic fully connected (dense) top
elif option == 3:
image_input = tf.keras.Input(shape=x_train.shape[1:], name='image_input')
x = MobileNet(weights='imagenet', include_top=False)(image_input)
x = tf.keras.layers.Flatten(name='flatten')(x)
x = tf.keras.layers.Dense(4096, activation='relu', name='fc1')(x)
x = tf.keras.layers.Dense(4096, activation='relu', name='fc2')(x)
x = tf.keras.layers.Dense(num_classes, activation='softmax', name='predictions')(x)
model = tf.keras.Model(inputs=image_input, outputs=x)
model.summary()
# Select loss, optimizer, metric
model.compile(loss='categorical_crossentropy',
optimizer=tf.train.AdamOptimizer(0.001),
metrics=['accuracy'])
# Train
start = timer()
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
# callbacks=[TensorBoardColabCallback(tbc)]
# Evaluate
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
nb_validation_samples = 50000
#%%
# model setup
def top5_acc(y_true, y_pred):
return metrics.top_k_categorical_accuracy(y_true, y_pred, k=5)
print('Building model...')
t = time.time()
#model = ResNet50(weights='../resnet50_weights_tf_dim_ordering_tf_kernels.h5')
model = MobileNet(weights='../mobilenet_1_0_224_tf.h5')
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy', top5_acc])
t = time.time() - t
#model.summary()
print('model build time: %f s' % t)
#%%
img_path = '../test_images/wine.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
