import numpy as np
from tensorflow.contrib.keras.api.keras import layers
from tensorflow.contrib.keras.api.keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
from tensorflow.contrib.keras.api.keras.models import Model, load_model
from tensorflow.contrib.keras.api.keras.preprocessing import image
from resnet_utils import *
from tensorflow.contrib.keras.api.keras.initializers import glorot_uniform
import tensorflow.contrib.keras.api.keras.backend as K
K.set_image_data_format('channels_last')
K.set_learning_phase(1)
def identity_block(X, f, filters, stage, block):
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
F1, F2, F3 = filters
X_shortcut = X
X = Conv2D(filters=F1,
kernel_size=(1, 1),
strides=(1, 1),
padding='valid',
name=conv_name_base + '2a',
kernel_initializer=glorot_uniform(seed=0))(X)
X = BatchNormalization(axis=3, name=bn_name_base + '2a')(X)
X = Activation('relu')(X)
X = Conv2D(filters=F2,
def main_fun(args, ctx):
import numpy
import os
import tensorflow as tf
import tensorflow.contrib.keras as keras
from tensorflow.contrib.keras.api.keras import backend as K
from tensorflow.contrib.keras.api.keras.models import Sequential, load_model, save_model
from tensorflow.contrib.keras.api.keras.layers import Dense, Dropout
from tensorflow.contrib.keras.api.keras.optimizers import RMSprop
from tensorflow.contrib.keras.python.keras.callbacks import LambdaCallback, TensorBoard
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def
from tensorflowonspark import TFNode
cluster, server = TFNode.start_cluster_server(ctx)
if ctx.job_name == "ps":
server.join()
elif ctx.job_name == "worker":
def generate_rdd_data(tf_feed, batch_size):
print("generate_rdd_data invoked")
while True:
batch = tf_feed.next_batch(batch_size)
imgs = []
lbls = []
for item in batch:
imgs.append(item[0])
lbls.append(item[1])
images = numpy.array(imgs).astype('float32') / 255
labels = numpy.array(lbls).astype('float32')
yield (images, labels)
with tf.device(
tf.train.replica_device_setter(
worker_device="/job:worker/task:%d" % ctx.task_index,
cluster=cluster)):
IMAGE_PIXELS = 28
batch_size = 100
num_classes = 10
# the data, shuffled and split between train and test sets
if args.input_mode == 'tf':
from tensorflow.contrib.keras.api.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
else: # args.mode == 'spark'
x_train = tf.placeholder(tf.float32,
[None, IMAGE_PIXELS * IMAGE_PIXELS],
name="x_train")
y_train = tf.placeholder(tf.float32, [None, 10],
name="y_train")
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784, )))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(),
metrics=['accuracy'])
saver = tf.train.Saver()
with tf.Session(server.target) as sess:
K.set_session(sess)
def save_checkpoint(epoch, logs=None):
if epoch == 1:
tf.train.write_graph(sess.graph.as_graph_def(),
args.model_dir, 'graph.pbtxt')
saver.save(sess,
os.path.join(args.model_dir, 'model.ckpt'),
global_step=epoch * args.steps_per_epoch)
ckpt_callback = LambdaCallback(on_epoch_end=save_checkpoint)
tb_callback = TensorBoard(log_dir=args.model_dir,
histogram_freq=1,
write_graph=True,
write_images=True)
# add callbacks to save model checkpoint and tensorboard events (on worker:0 only)
callbacks = [ckpt_callback, tb_callback
] if ctx.task_index == 0 else None
if args.input_mode == 'tf':
# train & validate on in-memory data
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=args.epochs,
verbose=1,
validation_data=(x_test, y_test),
callbacks=callbacks)
else: # args.input_mode == 'spark':
# train on data read from a generator which is producing data from a Spark RDD
tf_feed = TFNode.DataFeed(ctx.mgr)
history = model.fit_generator(
generator=generate_rdd_data(tf_feed, batch_size),
steps_per_epoch=args.steps_per_epoch,
epochs=args.epochs,
verbose=1,
callbacks=callbacks)
if args.export_dir and ctx.job_name == 'worker' and ctx.task_index == 0:
# save a local Keras model, so we can reload it with an inferencing learning_phase
save_model(model, "tmp_model")
# reload the model
K.set_learning_phase(False)
new_model = load_model("tmp_model")
# export a saved_model for inferencing
builder = saved_model_builder.SavedModelBuilder(
args.export_dir)
signature = predict_signature_def(
inputs={'images': new_model.input},
outputs={'scores': new_model.output})
builder.add_meta_graph_and_variables(
sess=sess,
tags=[tag_constants.SERVING],
signature_def_map={'predict': signature},
clear_devices=True)
builder.save()
if args.input_mode == 'spark':
tf_feed.terminate()
cam = np.maximum(cam, 0)
heatmap = cam / np.max(cam)
# Return to BGR [0..255] from the preprocessed image
image = image[0, :]
image -= np.min(image)
image = np.minimum(image, 255)
cam = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET)
cam = np.float32(cam) + np.float32(image)
cam = 255 * cam / np.max(cam)
return np.uint8(cam), heatmap
preprocessed_input = load_image(find(INPUT, INPUT_FOLDER))
k.set_learning_phase(0)
model = load_model('cifarClassification.h5')
layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]])
predictions = model.predict(preprocessed_input)
top_3 = decode_predictions(predictions)[0][0:3]
print('Predicted class:')
for x in range(0, len(top_3)):
print('%s with probability %.2f' % (top_3[x][0], top_3[x][1]))
predicted_class = np.argmax(predictions)
cam, heatmap = grad_cam(model, preprocessed_input, predicted_class, layer_name)
cv2.imwrite(
OUTPUT_FOLDER + "gradcam_" + INPUT[:-5] + "_" + layer_name + ".jpg", cam)
print('Gradiant class activation image saved in the current directory!')
mb_size = 64
Z_dim = 64
y_dim = 10
h_dim = 128
# x_train = x_train / 255.
# x_test = x_test / 255.
logs_path = 'logs'
if not os.path.isdir(logs_path):
os.makedirs(logs_path)
if not os.path.exists('out/'):
os.makedirs('out/')
K.set_learning_phase(True)
# Discriminator Net model
def discriminator(X_dim, y_dim):
df_dim = 64
x_in = Input(shape=(X_dim), name='X_input')
y_in = Input(shape=(y_dim, ), name='Y_input')
D_h = LeakyReLU(0.2)(Convolution2D(df_dim,
kernel_size=(5, 5),
strides=(2, 2),
padding='same')(x_in))
D_h = LeakyReLU(0.2)(BatchNormalization()(Convolution2D(
df_dim * 2, kernel_size=(5, 5), strides=(2, 2), padding='same')(D_h)))
D_h = LeakyReLU(0.2)(BatchNormalization()(Convolution2D(
df_dim * 2, kernel_size=(5, 5), strides=(2, 2), padding='same')(D_h)))