from tensorflow.keras import layers from tensorflow.keras import Input, Model lstm = layers.LSTM(32) left_input = Input(shape=(None, 128)) left_output = lstm(left_input) right_input = Input(shape=(None, 128)) right_output = lstm(right_input) merged = layers.concatenate([left_output, right_output], axis=-1) predictions = layers.Dense(1, activation='sigmoid')(merged) model = Model([left_input, right_input], predictions) model.summary() # model.fit([left_data, right_data], targets)
x = bottleneck_block(64, x)
# Double the size of filters and reduce feature maps by 75% (strides=2, 2) to fit the next Residual Group
x = conv_block(128, x)
# Second Residual Block Group of 128 filters
for _ in range(3):
x = bottleneck_block(128, x)
# Double the size of filters and reduce feature maps by 75% (strides=2, 2) to fit the next Residual Group
x = conv_block(256, x)
# Third Residual Block Group of 256 filters
for _ in range(22):
x = bottleneck_block(256, x)
# Double the size of filters and reduce feature maps by 75% (strides=2, 2) to fit the next Residual Group
x = conv_block(512, x)
# Fourth Residual Block Group of 512 filters
for _ in range(2):
x = bottleneck_block(512, x)
# Now Pool at the end of all the convolutional residual blocks
x = layers.GlobalAveragePooling2D()(x)
# Final Dense Outputting Layer for 1000 outputs
outputs = layers.Dense(1000, activation='softmax')(x)
model = Model(inputs, outputs)
def _update_models(self):
item_function = self.layers[1](self.layers[0](self.inputs[1]))
self.item_model = Model(self.inputs[1], item_function)
print(self.gru.weights)
x = inputs
# loop over the number of filters
for f in filters:
# apply a CONV => RELU => BN operation
x = Conv2D(f, (3, 3), strides=2, padding="same")(x)
x = LeakyReLU(alpha=0.2)(x)
x = BatchNormalization(axis=chanDim)(x)
# flatten the network and then construct our latent vector
volumeSize = K.int_shape(x)
x = Flatten()(x)
latent = Dense(latentDim)(x)
# build the encoder model
encoder = Model(inputs, latent, name="encoder")
# start building the decoder model which will accept the
# output of the encoder as its inputs
latentInputs = Input(shape=(latentDim, ))
x = Dense(np.prod(volumeSize[1:]))(latentInputs)
x = Reshape((volumeSize[1], volumeSize[2], volumeSize[3]))(x)
# loop over our number of filters again, but this time in
# reverse order
for f in filters[::-1]:
# apply a CONV_TRANSPOSE => RELU => BN operation
x = Conv2DTranspose(f, (3, 3), strides=2, padding="same")(x)
x = LeakyReLU(alpha=0.2)(x)
x = BatchNormalization(axis=chanDim)(x)
def build_stage2_generator():
"""
Create Stage-II generator containing the CA Augmentation Network,
the image encoder and the generator network
"""
# 1. CA Augmentation Network
input_layer = Input(shape=(1024, ))
input_lr_images = Input(shape=(64, 64, 3))
ca = Dense(256)(input_layer)
mean_logsigma = LeakyReLU(alpha=0.2)(ca)
c = Lambda(generate_c)(mean_logsigma)
# 2. Image Encoder
x = ZeroPadding2D(padding=(1, 1))(input_lr_images)
x = Conv2D(128, kernel_size=(3, 3), strides=1, use_bias=False)(x)
x = ReLU()(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(256, kernel_size=(4, 4), strides=2, use_bias=False)(x)
x = BatchNormalization()(x)
x = ReLU()(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(512, kernel_size=(4, 4), strides=2, use_bias=False)(x)
x = BatchNormalization()(x)
x = ReLU()(x)
# 3. Joint
c_code = Lambda(joint_block)([c, x])
x = ZeroPadding2D(padding=(1, 1))(c_code)
x = Conv2D(512, kernel_size=(3, 3), strides=1, use_bias=False)(x)
x = BatchNormalization()(x)
x = ReLU()(x)
# 4. Residual blocks
x = residual_block(x)
x = residual_block(x)
x = residual_block(x)
x = residual_block(x)
# 5. Upsampling blocks
x = UpSampling2D(size=(2, 2))(x)
x = Conv2D(512, kernel_size=3, padding="same", strides=1,
use_bias=False)(x)
x = BatchNormalization()(x)
x = ReLU()(x)
x = UpSampling2D(size=(2, 2))(x)
x = Conv2D(256, kernel_size=3, padding="same", strides=1,
use_bias=False)(x)
x = BatchNormalization()(x)
x = ReLU()(x)
x = UpSampling2D(size=(2, 2))(x)
x = Conv2D(128, kernel_size=3, padding="same", strides=1,
use_bias=False)(x)
x = BatchNormalization()(x)
x = ReLU()(x)
x = UpSampling2D(size=(2, 2))(x)
x = Conv2D(64, kernel_size=3, padding="same", strides=1, use_bias=False)(x)
x = BatchNormalization()(x)
x = ReLU()(x)
x = Conv2D(3, kernel_size=3, padding="same", strides=1, use_bias=False)(x)
x = Activation('tanh')(x)
model = Model(inputs=[input_layer, input_lr_images],
outputs=[x, mean_logsigma])
return model
def train_hinsage(self, S, node_identifiers, label, batch_size, epochs):
"""
This function trains a HinSAGE model, implemented in Tensorflow.
It returns the trained HinSAGE model and a pandas datarame containing the embeddings generated for the train nodes.
Parameters
----------
S : StellarGraph Object
The graph on which HinSAGE trains its aggregator functions.
node_identifiers : list
Defines the nodes that HinSAGE uses to train its aggregation functions.
label: Pandas dataframe
Defines the label of the nodes used for training, with the index representing the nodes.
batch_size: int
batch size to train the neural network in which HinSAGE is implemented.
epochs: int
Number of epochs for the neural network.
"""
from stellargraph.layer import MeanHinAggregator, MaxHinAggregator, MaxPoolingHinAggregator
# The mapper feeds data from sampled subgraph to GraphSAGE model
train_node_identifiers = node_identifiers[:round(0.8 *
len(node_identifiers)
)]
train_labels = label.loc[train_node_identifiers]
validation_node_identifiers = node_identifiers[
round(0.8 * len(node_identifiers)):]
validation_labels = label.loc[validation_node_identifiers]
generator = HinSAGENodeGenerator(
S,
batch_size,
self.num_samples,
head_node_type=self.embedding_for_node_type)
train_gen = generator.flow(train_node_identifiers,
train_labels,
shuffle=True)
test_gen = generator.flow(validation_node_identifiers,
validation_labels)
# HinSAGE model
model = HinSAGE(layer_sizes=[self.embedding_size] *
len(self.num_samples),
generator=generator,
aggregator=MeanHinAggregator,
dropout=0)
x_inp, x_out = model.build()
# Final estimator layer
prediction = layers.Dense(units=1,
activation="sigmoid",
dtype='float32')(x_out)
# Create Keras model for training
model = Model(inputs=x_inp, outputs=prediction)
model.compile(
optimizer=optimizers.Adam(lr=1e-3),
loss=binary_crossentropy,
)
# Train Model
model.fit(train_gen,
epochs=epochs,
verbose=1,
validation_data=test_gen,
shuffle=False)
trained_model = Model(inputs=x_inp, outputs=x_out)
train_gen_not_shuffled = generator.flow(node_identifiers,
label,
shuffle=False)
embeddings_train = trained_model.predict(train_gen_not_shuffled)
train_emb = pd.DataFrame(embeddings_train, index=node_identifiers)
return trained_model, train_emb
def build_generator(options, name="Generator"):
initializer = tf.random_normal_initializer(0.0, 0.02)
inputs = Input(shape=(options.time_step, options.pitch_range, options.output_nc))
x = inputs
# (batch * 64 * 84 * 1)
x = layers.Lambda(padding, name="PADDING_1")(x)
# (batch * 70 * 90 * 1)
x = layers.Conv2D(
filters=options.gf_dim,
kernel_size=7,
strides=1,
padding="valid",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_1",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.ReLU()(x)
# (batch * 64 * 84 * 64)
x = layers.Conv2D(
filters=options.gf_dim * 2,
kernel_size=3,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_2",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.ReLU()(x)
# (batch * 32 * 42 * 128)
x = layers.Conv2D(
filters=options.gf_dim * 4,
kernel_size=3,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_3",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.ReLU()(x)
# (batch * 16 * 21 * 256)
for i in range(10):
# x = resnet_block(x, options.gf_dim * 4)
x = ResNetBlock()(x, options.gf_dim * 4, initializer)
# (batch * 16 * 21 * 256)
x = layers.Conv2DTranspose(
filters=options.gf_dim * 2,
kernel_size=3,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="DECONV2D_1",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.ReLU()(x)
# (batch * 32 * 42 * 128)
x = layers.Conv2DTranspose(
filters=options.gf_dim,
kernel_size=3,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="DECONV2D_2",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.ReLU()(x)
# (batch * 64 * 84 * 64)
x = layers.Lambda(padding, name="PADDING_2")(x)
# After padding, (batch * 70 * 90 * 64)
x = layers.Conv2D(
filters=options.output_nc,
kernel_size=7,
strides=1,
padding="valid",
kernel_initializer=initializer,
activation="sigmoid",
use_bias=False,
name="CONV2D_4",
)(x)
# (batch * 64 * 84 * 1)
outputs = x
return Model(inputs=inputs, outputs=outputs, name=name)
# On top of it we stick two fully-connected layers. Because we are facing a two-class classification problem, i.e. a binary classification problem, we will end our network with a sigmoid activation, so that the output of our network will be a single scalar between 0 and 1, encoding the probability that the current image is class 1 (as opposed to class 0).
# Flatten feature map to a 1-dim tensor so we can add fully connected layers
x = layers.Flatten()(x)
# Create a fully connected layer with ReLU activation and 512 hidden units
x = layers.Dense(512, activation='relu')(x)
# Create output layer with a single node and sigmoid activation
output = layers.Dense(1, activation='sigmoid')(x)
# Create model:
# input = input feature map
# output = input feature map + stacked convolution/maxpooling layers + fully
# connected layer + sigmoid output layer
model = Model(img_input, output)
model.summary()
from tensorflow.keras.optimizers import RMSprop
model.compile(loss='binary_crossentropy',
optimizer=RMSprop(lr=0.001),
metrics=['acc'])
# data generation
# Let's set up data generators that will read pictures in our source folders, convert them to float32 tensors, and feed them (with their labels) to our network. We'll have one generator for the training images and one for the validation images. Our generators will yield batches of 20 images of size 150x150 and their labels (binary).
#
# As you may already know, data that goes into neural networks should usually be normalized in some way to make it more amenable to processing by the network. (It is uncommon to feed raw pixels into a convnet.) In our case, we will preprocess our images by normalizing the pixel values to be in the [0, 1] range (originally all values are in the [0, 255] range).
#
# load weights into the agent
agent.load_model(file_path='models/{:s}/'.format(version), iteration=iteration)
'''
# make some moves
for i in range(3):
env.print_game()
action = agent.move(s)
next_s, _, _, _, _ = env.step(action)
s = next_s.copy()
env.print_game()
'''
# define temporary model to get intermediate outputs
layer_num = 1
model_temp = Model(inputs=agent._model.input,
outputs=agent._model.layers[layer_num].output)
output_temp = model_temp.predict(s.reshape(1, board_size, board_size,
frames))[0, :, :, :]
print('selected layer shape : ', output_temp.shape)
# save layer weights
plt.clf()
w = agent._model.layers[layer_num].weights[0].numpy()
nrows, ncols = (w.shape[2] * w.shape[3]) // 8, 8
fig, axs = plt.subplots(nrows, ncols, figsize=(17, 17))
for i in range(nrows):
for j in range(ncols):
axs[i][j].imshow(w[:, :, j % 2, i * (ncols // 2) + (j // 2)],
cmap='gray')
fig.savefig('images/weight_visual_{:s}_{:04d}_conv{:d}.png'\
.format(version, iteration, layer_num),
def CNN2(images, y, params=None):
print(params)
x_train, x_test, y_train, y_test = train_test_split(images,
y,
test_size=0.2,
stratify=y,
random_state=100
)
x_train = np.array(x_train)
x_test = np.array(x_test)
image_size = x_train.shape[1]
image_size2 = x_train.shape[2]
x_train = np.reshape(x_train, [-1, image_size, image_size2, 1])
x_test = np.reshape(x_test, [-1, image_size, image_size2, 1])
kernel = params["kernel"]
kernel2=int(kernel/2)
inputs = Input(shape=(image_size, image_size2, 1))
X = Conv2D(32, (kernel,kernel), activation='relu', name='conv0')(inputs)
X = Dropout(rate=params['dropout1'])(X)
X = Conv2D(64, (kernel, kernel), activation='relu', name='conv1')(X)
X = Dropout(rate=params['dropout2'])(X)
X = Conv2D(128, (kernel, kernel), activation='relu', name='conv2')(X)
X = Flatten()(X)
X = Dense(256, activation='relu', kernel_initializer='glorot_uniform')(X)
X = Dense(1024, activation='relu', kernel_initializer='glorot_uniform')(X)
X = Dense(2, activation='softmax', kernel_initializer='glorot_uniform')(X)
model = Model(inputs, X)
adam = Adam(params["learning_rate"])
model.compile(loss='binary_crossentropy',
optimizer=adam,
metrics=['acc'])
# Train the model.
hist = model.fit(
x_train,
y_train,
epochs=params["epoch"],
verbose=2,
validation_data=(x_test, y_test),
batch_size=params["batch"],
callbacks=[EarlyStopping(monitor='val_loss', mode='min', patience=10),
ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]
)
model.load_weights('best_model.h5')
y_test = np.argmax(y_test, axis=1)
Y_predicted = model.predict(x_test, verbose=0, use_multiprocessing=True, workers=12)
Y_predicted = np.argmax(Y_predicted, axis=1)
cf = confusion_matrix(y_test, Y_predicted)
return model, {"balanced_accuracy_val": balanced_accuracy_score(y_test, Y_predicted) * 100,
"TP_val": cf[0][0],
"FN_val": cf[0][1], "FP_val": cf[1][0], "TN_val": cf[1][1]
}
def nnUNet_2D(image_shape, feature_maps=32, max_fa=480, num_pool=8,
k_init='he_normal', optimizer="sgd", lr=0.002, n_classes=1):
"""Create nnU-Net 2D. This implementations tries to be a Keras version of the
original nnU-Net 2D presented in `nnU-Net Github <https://github.com/MIC-DKFZ/nnUNet>`_.
Parameters
----------
image_shape : 2D tuple
Dimensions of the input image.
feature_map : ints, optional
Feature maps to start with in the first level of the U-Net (will be
duplicated on each level).
max_fa : int, optional
Number of maximum feature maps allowed to used in conv layers.
num_pool : int, optional
number of pooling (downsampling) operations to do.
k_init : string, optional
Kernel initialization for convolutional layers.
optimizer : str, optional
Optimizer used to minimize the loss function. Posible options:
``sgd`` or ``adam``.
lr : float, optional
Learning rate value.
n_classes: int, optional
Number of classes.
Returns
-------
model : Keras model
Model containing the U-Net.
"""
dinamic_dim = (None,)*(len(image_shape)-1) + (image_shape[-1],)
x = Input(dinamic_dim)
#x = Input(image_shape)
inputs = x
l=[]
seg_outputs = []
fa_save = []
fa = feature_maps
# ENCODER
x = StackedConvLayers(x, fa, k_init, first_conv_stride=1)
fa_save.append(fa)
fa = fa*2 if fa*2 < max_fa else max_fa
l.append(x)
# conv_blocks_context
for i in range(num_pool-1):
x = StackedConvLayers(x, fa, k_init)
fa_save.append(fa)
fa = fa*2 if fa*2 < max_fa else max_fa
l.append(x)
# BOTTLENECK
x = StackedConvLayers(x, fa, k_init, first_conv_stride=(1,2))
# DECODER
for i in range(len(fa_save)):
# tu
if i == 0:
x = Conv2DTranspose(fa_save[-(i+1)], (1, 2), use_bias=False,
strides=(1, 2), padding='valid') (x)
else:
x = Conv2DTranspose(fa_save[-(i+1)], (2, 2), use_bias=False,
strides=(2, 2), padding='valid') (x)
x = concatenate([x, l[-(i+1)]])
# conv_blocks_localization
x = StackedConvLayers(x, fa_save[-(i+1)], k_init, first_conv_stride=1)
seg_outputs.append(Conv2D(n_classes, (1, 1), use_bias=False, activation="softmax") (x))
outputs = seg_outputs
model = Model(inputs=[inputs], outputs=[outputs])
# Select the optimizer
if optimizer == "sgd":
opt = tf.keras.optimizers.SGD(
lr=lr, momentum=0.99, decay=0.0, nesterov=False)
elif optimizer == "adam":
opt = tf.keras.optimizers.Adam(
lr=lr, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0,
amsgrad=False)
else:
raise ValueError("Error: optimizer value must be 'sgd' or 'adam'")
# Calculate the weigts as nnUNet does
################# Here we wrap the loss for deep supervision ############
# we need to know the number of outputs of the network
net_numpool = num_pool
# we give each output a weight which decreases exponentially (division by 2) as the resolution decreases
# this gives higher resolution outputs more weight in the loss
weights = np.array([1 / (2 ** i) for i in range(net_numpool)])
# we don't use the lowest 2 outputs. Normalize weights so that they sum to 1
mask = np.array([True] + [True if i < net_numpool - 1 else False for i in range(1, net_numpool)])
weights[~mask] = 0
weights = weights / weights.sum()
weights = weights[::-1]
################# END ###################
# Compile the model
model.compile(optimizer=opt, loss='binary_crossentropy',
metrics=[jaccard_index_softmax], loss_weights=list(weights))
return model
def CNN_Nature(images, y, param=None):
print(param)
x_train, x_test, y_train, y_test = train_test_split(images,
y,
test_size=0.2,
stratify=y,
random_state=100)
x_train = np.array(x_train)
x_test = np.array(x_test)
image_size = x_train.shape[1]
image_size2 = x_train.shape[2]
x_train = np.reshape(x_train, [-1, image_size, image_size2, 1])
x_test = np.reshape(x_test, [-1, image_size, image_size2, 1])
num_filters = param["filter"]
num_filters2 = param["filter2"]
kernel = param["kernel"]
inputs = Input(shape=(image_size, image_size2, 1))
print(x_train.shape)
out = Conv2D(filters=num_filters,
kernel_size=(kernel, kernel),
padding="same")(inputs)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = MaxPooling2D(strides=2, pool_size=2)(out)
out = Conv2D(filters=2 * num_filters,
kernel_size=(kernel, kernel),
padding="same")(out)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = MaxPooling2D(strides=2, pool_size=2)(out)
out = Conv2D(filters=4 * num_filters,
kernel_size=(kernel, kernel),
padding="same")(out)
out = BatchNormalization()(out)
out = Activation('relu')(out)
# layer 2
out2 = Conv2D(filters=num_filters2,
kernel_size=(kernel, kernel),
padding="same")(inputs)
out2 = BatchNormalization()(out2)
out2 = Activation('relu')(out2)
out2 = MaxPooling2D(strides=2, pool_size=2)(out2)
out2 = Conv2D(filters=2 * num_filters2,
kernel_size=(kernel, kernel),
padding="same")(out2)
out2 = BatchNormalization()(out2)
out2 = Activation('relu')(out2)
out2 = MaxPooling2D(strides=2, pool_size=2)(out2)
out2 = Conv2D(filters=4 * num_filters2,
kernel_size=(kernel, kernel),
padding="same")(out2)
out2 = BatchNormalization()(out2)
out2 = Activation('relu')(out2)
# final layer
outf = Concatenate()([out, out2])
out_f = AveragePooling2D(strides=2, pool_size=2)(outf)
out_f = Flatten()(out_f)
predictions = Dense(2, activation='softmax')(out_f)
# This creates a model that includes
# the Input layer and three Dense layers
model = Model(inputs=inputs, outputs=predictions)
adam = Adam(lr=param["learning_rate"])
# Compile the model.
model.compile(
optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'],
)
# Train the model.
hist = model.fit(
x_train,
y_train,
epochs=param["epoch"],
verbose=2,
validation_data=(x_test, y_test),
batch_size=param["batch"],
callbacks=[EarlyStopping(monitor='val_loss', mode='min', patience=10),
ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]
)
model.load_weights('best_model.h5')
y_test = np.argmax(y_test, axis=1)
Y_predicted = model.predict(x_test, verbose=0, use_multiprocessing=True, workers=12)
Y_predicted = np.argmax(Y_predicted, axis=1)
cf = confusion_matrix(y_test, Y_predicted)
return model, {"balanced_accuracy_val": balanced_accuracy_score(y_test, Y_predicted) * 100, "TP_val": cf[0][0],
"FN_val": cf[0][1], "FP_val": cf[1][0], "TN_val": cf[1][1]
}
strategy = tf.distribute.MirroredStrategy()
print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
with strategy.scope():
base_model = VGG16(weights='imagenet',
include_top=False,
input_shape=(32, 32, 3))
# Extract the last layer from third block of vgg16 model
last = base_model.get_layer('block3_pool').output
# Add classification layers on top of it
x = Flatten()(last)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
pred = Dense(10, activation='sigmoid')(x)
model = Model(base_model.input, pred)
# set the base model's layers to non-trainable
# uncomment next two lines if you don't want to
# train the base model
# for layer in base_model.layers:
# layer.trainable = False
# compile the model with a SGD/momentum optimizer
# and a very slow learning rate.
model.compile(loss='binary_crossentropy',
optimizer=tf.optimizers.SGD(lr=args.learning_rate,
momentum=0.9),
metrics=['accuracy'])
# prepare data augmentation configuration
def findModel(self):
#max val acccuracy, epoch at max val_acc, model type of max val_acc
mx = 0
epochAtMx = 0
modelType = ""
#Set initial hyperparameters for training runs
batch_size=16
n_epochs=10
lr = 0.001
models = ['vgg16','mobilenet','inception','inception_resnet','xception']
#create directory for saved model files with highest val_accuracy
save_dir = os.path.join(os.getcwd(), 'saved_models')
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
#model params to set for each training run
preprocessingFunc = None
model = None
removeLayer = 0
freezeLayers = 0
#batches to set for each training run
train_batches = None
valid_batches = None
test_batches = None
#Perform training for each model type, saving the best models and validation accuracies.
#Record best overall val_accuracy,epoch,model
#len(models)
for i in range(len(models)):
model_name = models[i]
#callback for saving checkpoint at best validation accuracy
model_filename = '%s_model.{epoch:03d}.h5' % model_name
filepath = os.path.join(save_dir, model_filename)
# prepare callback for model saving
checkpoint = ModelCheckpoint(filepath=filepath,
monitor='val_accuracy',
verbose=1,
save_best_only=True)
if model_name == 'vgg16':
preprocessingFunc = tf.keras.applications.vgg16.preprocess_input
model = tf.keras.applications.vgg16.VGG16()
removeLay = -2
freezeLay = -3
elif model_name == 'mobilenet':
preprocessingFunc = tf.keras.applications.mobilenet.preprocess_input
model = tf.keras.applications.mobilenet.MobileNet()
removeLay = -6
freezeLay = -5
elif model_name == 'inception':
preprocessingFunc = tf.keras.applications.inception_v3.preprocess_input
model = tf.keras.applications.inception_v3.InceptionV3()
removeLay = -1
freezeLay = -5
elif model_name == 'inception_resnet':
preprocessingFunc = tf.keras.applications.inception_resnet_v2.preprocess_input
model = tf.keras.applications.inception_resnet_v2.InceptionResNetV2()
removeLay = -1
freezeLay = -5
elif model_name == 'xception':
preprocessingFunc = tf.keras.applications.xception.preprocess_input
model = tf.keras.applications.xception.Xception()
removeLay = -1
freezeLay = -5
train_batches = ImageDataGenerator(preprocessing_function=preprocessingFunc) .flow_from_directory(directory="training/", target_size=(224,224), classes=self.labels, batch_size=batch_size)
valid_batches = ImageDataGenerator(preprocessing_function=preprocessingFunc) .flow_from_directory(directory="validation/", target_size=(224,224), classes=self.labels, batch_size=batch_size)
test_batches = ImageDataGenerator(preprocessing_function=preprocessingFunc) .flow_from_directory(directory="testing", target_size=(224,224), classes=self.labels, batch_size=batch_size, shuffle=False)
x = model.layers[removeLay].output # we remove the output. layer that we replace with the following line
output = Dense(units=len(self.labels), activation='softmax')(x)
model_new = Model(inputs=model.input, outputs=output)
for layer in model_new.layers[:freezeLay]: # retrain fc1 , fc2 and prediction layer
layer.trainable = False
model_new.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
#model_new.summary()
history_model = model_new.fit(x=train_batches,
validation_data=valid_batches,
validation_steps=len(valid_batches),
epochs=n_epochs,
verbose=1,callbacks=[checkpoint]
)
#print the max validation accuracy and the epoch at which it occured
for k in range(len(history_model.history['val_accuracy'])):
if history_model.history['val_accuracy'][k] > mx:
mx = history_model.history['val_accuracy'][k]
epochAtMx = k + 1
modelType = model_name
print('max val acc: {}'.format(mx))
print('at epoch: {}'.format(epochAtMx))
print('best model: {}'.format(modelType))
#Select and load the model with highest recorded val_accuracy
bestModelFile = modelType + ('_model.%03d.h5' % epochAtMx)
bestModel = keras.models.load_model('saved_models/'+bestModelFile)
print('best model file: {}'.format(bestModelFile))
#Set model attributes according to best model type
if modelType == 'vgg16':
preprocessingFunc = tf.keras.applications.vgg16.preprocess_input
removeLay = -2
freezeLay = -3
elif modelType == 'mobilenet':
preprocessingFunc = tf.keras.applications.mobilenet.preprocess_input
removeLay = -6
freezeLay = -5
elif modelType == 'inception':
preprocessingFunc = tf.keras.applications.inception_v3.preprocess_input
removeLay = -1
freezeLay = -5
elif modelType == 'inception_resnet':
preprocessingFunc = tf.keras.applications.inception_resnet_v2.preprocess_input
removeLay = -1
freezeLay = -5
elif modelType == 'xception':
preprocessingFunc = tf.keras.applications.xception.preprocess_input
removeLay = -1
freezeLay = -5
#Initialize new callback for best model
#callback for saving checkpoint at best validation accuracy
model_name = 'bestModel'
model_filename = '%s_model.{epoch:03d}.h5' % model_name
filepath = os.path.join(save_dir, model_filename)
# prepare callback for model saving
checkpoint = ModelCheckpoint(filepath=filepath,
monitor='val_accuracy',
verbose=1,
save_best_only=True)
#Update hyperparameters for tuning the best model
#Create new batches for best model.
#Then start training.
#set new number of epochs , batch size, lern rate for best model tuning
n_epochs=15
batch_size = 32
lr = 0.00001
#create batches for new training round with updated batch size
train_batches = ImageDataGenerator(rotation_range=5,horizontal_flip=True,preprocessing_function=preprocessingFunc) .flow_from_directory(directory="training/", target_size=(224,224), classes=self.labels, batch_size=batch_size)
valid_batches = ImageDataGenerator(rotation_range=5,horizontal_flip=True,preprocessing_function=preprocessingFunc) .flow_from_directory(directory="validation/", target_size=(224,224), classes=self.labels, batch_size=batch_size)
test_batches = ImageDataGenerator(preprocessing_function=preprocessingFunc) .flow_from_directory(directory="testing", target_size=(224,224), classes=self.labels, batch_size=batch_size, shuffle=False)
opt = keras.optimizers.Adam(learning_rate=lr)
bestModel.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
history_bestModel = bestModel.fit(x=train_batches,
steps_per_epoch=len(train_batches),
validation_data=valid_batches,
validation_steps=len(valid_batches),
epochs=n_epochs,
verbose=1,callbacks=[checkpoint]
)
#Record best model's top validation accuracy and epoch.
#Model h5 file saved by checkpoint callback.
#print the max validation accuracy and the epoch at which it occured
for k in range(len(history_bestModel.history['val_accuracy'])):
if history_bestModel.history['val_accuracy'][k] > mx:
mx = history_bestModel.history['val_accuracy'][k]
epochAtMx = k + 1
modelType = model_name
print('max val acc: {}'.format(mx))
print('at epoch: {}'.format(epochAtMx))
print('best model: {}'.format(modelType))
#save model summary
summary_file = 'bestModel_summary.txt'
with open(summary_file, 'w') as f:
with redirect_stdout(f):
bestModel.summary()
#Show confusion matrix and training history of best model training run with tuned hyperparameters.
predictions_bestModel = bestModel.predict(x=test_batches, steps=len(test_batches), verbose=0)
cm = confusion_matrix(y_true=test_batches.classes, y_pred=predictions_bestModel.argmax(axis=1))
self.plot_confusion_matrix(cm=cm, classes=list(test_batches.class_indices.keys()),name="confusion_matrix_bestModel.png")
plt.show()
self.plot_history(history_bestModel,name="history_bestModel")
plt.show()
return modelType, epochAtMx
for layer in pre_trained_model.layers:
layer.trainable = False
# pre_trained_model.summary()
last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output
x = layers.Flatten()(last_output)
x = layers.Dense(1024, activation='relu')(x)
x = layers.Dropout(0.2)(x)
x = layers.Dense(classes, activation='softmax')(x)
model = Model(pre_trained_model.input, x)
model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=0.0001),
loss='categorical_crossentropy',
metrics=['accuracy', 'mae'])
history = model.fit_generator(
train_generator,
steps_per_epoch=total_count // batch_size,
epochs=100,
)
plots = ['mae']
for plot in plots:
metric = history.history[plot]
# val_metric = history.history[f"val_{plot}"]
base_model = InceptionResNetV2(
weights='imagenet',
include_top=False,
input_shape=(image_dim, image_dim, 3)
)
add_model = Sequential([
Conv2D(256, kernel_size = (3,3), padding='same'),
MaxPooling2D(pool_size = (2,2), padding='same'),
Flatten(),
Dense(256),
Activation(activation='relu'),
Dropout(0.3),
Dense(train_data.num_classes, activation = 'softmax')
])
model = Model(inputs=base_model.input, outputs=add_model(base_model.output))
model.summary()
#--------------------------------------------------------------------------------------------------------------------------------
# Optimizers
#--------------------------------------------------------------------------------------------------------------------------------
sgd = SGD(
lr=1e-4,
momentum=0.9,
)
#--------------------------------------------------------------------------------------------------------------------------------
# Compile Model
#--------------------------------------------------------------------------------------------------------------------------------
model.compile(
optimizer = sgd, # Test adam and nadam/ best result rmsprop
loss = categorical_crossentropy,
metrics = ['accuracy'],
# We create and train our DeepGraphInfomax model (docs). Note that the loss used here must always be
# tf.nn.sigmoid_cross_entropy_with_logits.
fullbatch_generator = FullBatchNodeGenerator(G, sparse=False)
gcn_model = GCN(layer_sizes=[2],
activations=["relu"],
generator=fullbatch_generator)
corrupted_generator = CorruptedGenerator(fullbatch_generator)
gen = corrupted_generator.flow(G.nodes())
infomax = DeepGraphInfomax(gcn_model, corrupted_generator)
x_in, x_out = infomax.in_out_tensors()
model = Model(inputs=x_in, outputs=x_out)
model.compile(loss=tf.nn.sigmoid_cross_entropy_with_logits,
optimizer=Adam(lr=1e-3))
epochs = 100
es = EarlyStopping(monitor="loss", min_delta=0, patience=20)
history = model.fit(gen, epochs=epochs, verbose=0, callbacks=[es])
# plot_history(history)
x_emb_in, x_emb_out = gcn_model.in_out_tensors()
# for full batch models, squeeze out the batch dim (which is 1)
x_out = tf.squeeze(x_emb_out, axis=0)
emb_model = Model(inputs=x_emb_in, outputs=x_out)
# model = load_model('./checkpoints/weights-20200328-faces.hdf5')
# Define a new model
input_shape = Input((FRAME_COUNT, FRAME_WIDTH, FRAME_HEIGHT, FRAME_CHANNELS))
LSTM = ConvLSTM2D(3,(1,1), strides=(1,1), padding='same', activation='relu',\
recurrent_activation='hard_sigmoid')(input_shape)
LSTM = MaxPooling2D((2, 2), strides=(1, 1), padding='same')(LSTM)
XCEPT = Xception(include_top=False, weights='imagenet', pooling='avg')(LSTM)
XCEPT = Flatten()(XCEPT)
OUT = Dropout(0.80)(XCEPT)
OUT = Dense(1, activation='sigmoid')(OUT)
model = Model(input_shape, OUT)
# Define custom metric
# Clip the ends of the predicted probabilities
def clip_loss(y_true, y_pred):
clip_loss = binary_crossentropy(y_true, K.clip(y_pred, 0.01, 0.99))
return clip_loss
# Compile and view model summary
optimizer = Adam(learning_rate=1e-5)
model.compile(optimizer, loss='binary_crossentropy', metrics=['accuracy'])
model.summary()
# Model checkpoint
def train_model(Gnx, train_data, test_data, all_features):
output_results = {}
from collections import Counter
#TODO: save size of dataset, train_data, and test data
#save the count of each subject in the blocks
print(len(train_data), len(test_data))
subject_groups_train = Counter(train_data['subject'])
subject_groups_test = Counter(test_data['subject'])
output_results['train_size'] = len(train_data)
output_results['test_size'] = len(test_data)
output_results['subject_groups_train'] = subject_groups_train
output_results['subject_groups_test'] = subject_groups_test
#node_features = train_data[feature_names]
#print (feature_names)
G = sg.StellarGraph(Gnx, node_features=all_features)
#TODO: save graph info
print(G.info())
print("writing graph.dot")
#write_dot(Gnx,"graph.dot")
output_results['graph_info'] = G.info()
print("building the graph generator...")
batch_size = 50
num_samples = [10, 5]
generator = GraphSAGENodeGenerator(G, batch_size, num_samples)
#generator = HinSAGENodeGenerator(G, batch_size, num_samples)
target_encoding = feature_extraction.DictVectorizer(sparse=False)
train_targets = target_encoding.fit_transform(
train_data[["subject"]].to_dict('records'))
print(np.unique(train_data["subject"].to_list()))
class_weights = class_weight.compute_class_weight(
'balanced', np.unique(train_data["subject"].to_list()),
train_data["subject"].to_list())
print('class_weights', class_weights)
test_targets = target_encoding.transform(test_data[["subject"
]].to_dict('records'))
train_gen = generator.flow(train_data.index, train_targets, shuffle=True)
graphsage_model = GraphSAGE(
#graphsage_model = HinSAGE(
#layer_sizes=[32, 32],
layer_sizes=[80, 80],
generator=generator, #train_gen,
bias=True,
dropout=0.5,
)
print("building model...")
#x_inp, x_out = graphsage_model.build(flatten_output=True)
x_inp, x_out = graphsage_model.build()
prediction = layers.Dense(units=train_targets.shape[1],
activation="softmax")(x_out)
model = Model(inputs=x_inp, outputs=prediction)
print("compiling model...")
model.compile(
optimizer=optimizers.Adam(lr=0.005),
loss=losses.categorical_crossentropy,
metrics=["acc", metrics.categorical_accuracy],
)
print("testing the model...")
test_gen = generator.flow(test_data.index, test_targets)
history = model.fit_generator(
train_gen,
epochs=EPOCH,
validation_data=test_gen,
verbose=2,
shuffle=True,
class_weight=class_weights,
)
# save test metrics
test_metrics = model.evaluate_generator(test_gen)
print("\nTest Set Metrics:")
output_results['test_metrics'] = []
for name, val in zip(model.metrics_names, test_metrics):
output_results['test_metrics'].append({'name': name, 'val:': val})
print("\t{}: {:0.4f}".format(name, val))
test_nodes = test_data.index
test_mapper = generator.flow(test_nodes)
test_predictions = model.predict_generator(test_mapper)
node_predictions = target_encoding.inverse_transform(test_predictions)
results = pd.DataFrame(node_predictions, index=test_nodes).idxmax(axis=1)
df = pd.DataFrame({
"Predicted": results,
"True": test_data['subject']
}) #, "program":test_data['program']})
clean_result_labels = df["Predicted"].map(
lambda x: x.replace('subject=', ''))
# save predicted labels
pred_labels = np.unique(clean_result_labels.values)
#pred_program = np.unique(df['program'].values)
# save predictions per label
precision, recall, f1, _ = skmetrics.precision_recall_fscore_support(
df['True'].values,
clean_result_labels.values,
average=None,
labels=pred_labels)
output_results['classifier'] = []
for lbl, prec, rec, fm in zip(pred_labels, precision, recall, f1):
output_results['classifier'].append({
'label': lbl,
'precision': prec,
'recall': rec,
'fscore': fm
})
print(output_results['classifier'])
print(pred_labels)
print('precision: {}'.format(precision))
print('recall: {}'.format(recall))
print('fscore: {}'.format(f1))
return generator, model, x_inp, x_out, history, target_encoding, output_results
def get_nmf_model(self):
num_factors = self.num_factors
num_layers = self.num_layers
layer1_dim = self.num_factors * (2**(num_layers - 1))
num_users = self.num_users
num_items = self.num_items
num_genres = self.num_genres
# input layer
user_input_layer = layers.Input(shape=(1, ),
dtype='int32',
name='user_input')
item_input_layer = layers.Input(shape=(1, ),
dtype='int32',
name='item_input')
genre_input_layer = layers.Input(shape=(None, ),
dtype='int32',
name='genre_input')
# GMF embedding layer
GMF_user_embedding = layers.Embedding(
input_dim=num_users,
output_dim=num_factors,
embeddings_regularizer=regularizers.l2(0.),
name='GMF_user_embedding')(user_input_layer)
GMF_item_embedding = layers.Embedding(
input_dim=num_items,
output_dim=self.num_movie_factors,
embeddings_regularizer=regularizers.l2(0.),
name='GMF_item_embedding')(item_input_layer)
GMF_genre_embedding = layers.Embedding(
input_dim=num_genres,
output_dim=self.num_movie_factors,
mask_zero=True,
embeddings_regularizer=regularizers.l2(0.),
name='GMF_genre_embedding')(genre_input_layer)
GMF_genre_emb_mean = tf.reduce_mean(GMF_genre_embedding, 1)
# MLP embedding layer
MLP_user_embedding = layers.Embedding(
input_dim=num_users,
output_dim=num_factors,
embeddings_regularizer=regularizers.l2(0.),
name='MLP_user_embedding')(user_input_layer)
MLP_item_embedding = layers.Embedding(
input_dim=num_items,
output_dim=self.num_movie_factors,
embeddings_regularizer=regularizers.l2(0.),
name='MLP_item_embedding')(item_input_layer)
MLP_genre_embedding = layers.Embedding(
input_dim=num_genres,
output_dim=self.num_movie_factors,
mask_zero=True,
embeddings_regularizer=regularizers.l2(0.),
name='MLP_genre_embedding')(genre_input_layer)
MLP_genre_emb_mean = tf.reduce_mean(MLP_genre_embedding, 1)
# GMF
GMF_user_latent = layers.Flatten()(GMF_user_embedding)
GMF_item_latent = layers.Flatten()(GMF_item_embedding)
GMF_genre_latent = layers.Flatten()(GMF_genre_emb_mean)
GMF_movie_latent = layers.concatenate(
[GMF_item_latent, GMF_genre_latent])
# MLP
MLP_user_latent = layers.Flatten()(MLP_user_embedding)
MLP_item_latent = layers.Flatten()(MLP_item_embedding)
MLP_genre_latent = layers.Flatten()(MLP_genre_emb_mean)
MLP_movie_latent = layers.concatenate(
[MLP_item_latent, MLP_genre_latent])
# gmf - element wise product
GMF_vector = layers.multiply([GMF_user_latent, GMF_movie_latent])
GMF_vector = layers.BatchNormalization()(GMF_vector)
# mlp
MLP_vector = layers.concatenate([MLP_user_latent, MLP_movie_latent])
MLP_vector = layers.BatchNormalization()(MLP_vector)
for i in range(num_layers - 1):
MLP_vector = layers.Dense((int)(layer1_dim / (2**i)),
activation='tanh',
name='layer%s' % str(i + 1))(MLP_vector)
MLP_vector = layers.BatchNormalization()(MLP_vector)
#NeuMF layer
NeuMF_vector = layers.concatenate([GMF_vector, MLP_vector])
prediction = layers.Dense(1, activation='tanh',
name='prediction')(NeuMF_vector)
model = Model([user_input_layer, item_input_layer, genre_input_layer],
prediction)
self.nmf_model = model
self.nmf_model = self.set_nmf_weight()
return self.nmf_model
def build_discriminator_classifier(options, name="Discriminator_Classifier"):
initializer = tf.random_normal_initializer(0.0, 0.02)
inputs = Input(shape=(options.time_step, options.pitch_range, options.output_nc))
x = inputs
# (batch * 64, 84, 1)
x = layers.Conv2D(
filters=options.df_dim,
kernel_size=[1, 12],
strides=[1, 12],
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_1",
)(x)
x = layers.LeakyReLU(alpha=0.2)(x)
# (batch * 64 * 7 * 64)
x = layers.Conv2D(
filters=options.df_dim * 2,
kernel_size=[4, 1],
strides=[4, 1],
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_2",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.LeakyReLU(alpha=0.2)(x)
# (batch * 16 * 7 * 128)
x = layers.Conv2D(
filters=options.df_dim * 4,
kernel_size=[2, 1],
strides=[2, 1],
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_3",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.LeakyReLU(alpha=0.2)(x)
# (batch * 8 * 7 * 256)
x = layers.Conv2D(
filters=options.df_dim * 8,
kernel_size=[8, 1],
strides=[8, 1],
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_4",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.LeakyReLU(alpha=0.2)(x)
# (batch * 1 * 7 * 512)
x = layers.Conv2D(
filters=2,
kernel_size=[1, 7],
strides=[1, 7],
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_5",
)(x)
# (batch * 1 * 1 * 2)
x = tf.reshape(x, [-1, 2])
# (batch * 2)
outputs = x
return Model(inputs=inputs, outputs=outputs, name=name)
def __init__(self,
num_workers: int = 1,
model: keras.Model = None,
optimizer: Union[keras.optimizers.Optimizer, str] = None,
loss: Union[keras.losses.Loss, str] = None,
metrics: Union[List[keras.metrics.Metric], List[str]] = None,
feature_columns: Union[str, List[str]] = None,
feature_types: Optional[Union[DType, List[DType]]] = None,
feature_shapes: Optional[Union[TensorShape,
List[TensorShape]]] = None,
label_column: str = None,
label_type: Optional[tf.DType] = None,
label_shape: Optional[tf.TensorShape] = None,
batch_size: int = None,
num_epochs: int = None,
shuffle: bool = True,
config: Dict = None):
"""
A scikit-learn like API to distributed training Tensorflow Keras model. In the backend it
leverage the ray.sgd.TorchTrainer.
:param num_workers: the number of workers for distributed model training
:param model: the model, it should be instance of tensorflow.keras.Model. We do not support
multiple output models.
:param optimizer: the optimizer, it should be keras.optimizers.Optimizer instance or str.
We do not support multiple optimizers currently.
:param loss: the loss, it should be keras.losses.Loss instance or str. We do not support
multiple losses.
:param metrics: the metrics list. It could be None, a list of keras.metrics.Metric instance
or a list of str.
:param feature_columns: the feature columns name.
:param feature_types: the type for each feature input. It must match the length of the
feature_columns if provided. It will be tf.float32 by default.
:param feature_shapes: the shape for each feature input. It must match the length of the
feature_columns
:param label_column: the label column name.
:param label_type: the label type, it will be tf.float32 by default.
:param label_shape: the label shape.
:param batch_size: the batch size
:param num_epochs: the number of epochs
:param shuffle: whether input dataset should be shuffle, True by default.
:param config: extra config will fit into TFTrainer.
"""
self._num_workers: int = num_workers
# model
assert model is not None, "model must be not be None"
if isinstance(model, keras.Model):
self._serialized_model = model.to_json()
else:
raise Exception(
"Unsupported parameter, we only support tensorflow.keras.Model"
)
# optimizer
# TODO: we should support multiple optimizers for multiple outputs model
assert optimizer is not None, "optimizer must not be None"
if isinstance(optimizer, str):
# it is a str represents the optimizer
_optimizer = optimizer
elif isinstance(optimizer, keras.optimizers.Optimizer):
_optimizer = keras.optimizers.serialize(optimizer)
else:
raise Exception(
"Unsupported parameter, we only support keras.optimizers.Optimizer subclass "
"instance or a str to represent the optimizer")
self._serialized_optimizer = _optimizer
# loss
# TODO: we should support multiple losses for multiple outputs model
assert loss is not None, "loss must not be None"
if isinstance(loss, str):
_loss = loss
elif isinstance(loss, keras.losses.Loss):
_loss = keras.losses.serialize(loss)
else:
raise Exception(
"Unsupported parameter, we only support keras.losses.Loss subclass "
"instance or a str to represents the loss)")
self._serialized_loss = _loss
# metrics
if metrics is None:
_metrics = None
else:
assert isinstance(metrics, list), "metrics must be a list"
if isinstance(metrics[0], str):
_metrics = metrics
elif isinstance(metrics[0], keras.metrics.Metric):
_metrics = [keras.metrics.serialize(m) for m in metrics]
else:
raise Exception(
"Unsupported parameter, we only support list of "
"keras.metrics.Metrics instances or list of str to")
self._serialized_metrics = _metrics
self._feature_columns = feature_columns
self._feature_types = feature_types
self._feature_shapes = feature_shapes
self._label_column = label_column
self._label_type = label_type
self._label_shape = label_shape
_config = {"batch_size": batch_size}
_config.update(config)
self._config = _config
self._num_epochs: int = num_epochs
self._shuffle: bool = shuffle
self._trainer: TFTrainer = None
class BaseModel:
def __init__(self, train_generator=None, subpixel=False, **kwargs):
self.train_generator = train_generator
self.subpixel = subpixel
if "skip_init" not in kwargs:
config = self.train_generator.get_config()
if self.train_model is NotImplemented:
self.__init_input__(config["image_shape"])
self.__init_model__()
self.__init_train_model__()
if self.train_generator is not None:
if self.subpixel:
output_sigma = config["output_sigma"]
else:
output_sigma = None
self.__init_predict_model__(
config["output_shape"],
config["keypoints_shape"],
config["downsample_factor"],
config["output_sigma"],
)
train_model = NotImplemented
def __init_input__(self, image_shape):
self.input_shape = image_shape
self.inputs = Input(self.input_shape)
def __init_train_model__(self):
if isinstance(self.train_model, Model):
self.compile = self.train_model.compile
self.n_outputs = len(self.train_model.outputs)
else:
raise TypeError("self.train_model must be keras.Model class")
def __init_model__(self):
raise NotImplementedError(
"__init_model__ method must be" "implemented to define `self.train_model`"
)
def __init_predict_model__(
self,
output_shape,
keypoints_shape,
downsample_factor,
output_sigma=None,
**kwargs
):
outputs = self.train_model(self.inputs)
if isinstance(outputs, list):
outputs = outputs[-1]
if self.subpixel:
kernel_size = np.min(output_shape)
kernel_size = (kernel_size // largest_factor(kernel_size)) + 1
sigma = output_sigma
keypoints = SubpixelMaxima2D(
kernel_size,
sigma,
upsample_factor=100,
index=keypoints_shape[0],
coordinate_scale=2 ** downsample_factor,
confidence_scale=255.0,
)(outputs)
else:
keypoints = Maxima2D(
index=keypoints_shape[0],
coordinate_scale=2 ** downsample_factor,
confidence_scale=255.0,
)(outputs)
self.predict_model = Model(self.inputs, keypoints, name=self.train_model.name)
self.predict = self.predict_model.predict
self.predict_generator = self.predict_model.predict_generator
self.predict_on_batch = self.predict_model.predict_on_batch
# Fix for passing model to callbacks.ModelCheckpoint
if hasattr(self.train_model, "_in_multi_worker_mode"):
self._in_multi_worker_mode = self.train_model._in_multi_worker_mode
def fit(
self,
batch_size,
validation_batch_size=1,
callbacks=[],
epochs=1,
use_multiprocessing=False,
n_workers=1,
steps_per_epoch=None,
**kwargs
):
"""
Trains the model for a given number of epochs (iterations on a dataset).
Parameters
----------
batch_size : int
Number of samples per training update.
validation_batch_size : int
Number of samples per validation batch used when evaluating the model.
callbacks : list or None
List of keras.callbacks.Callback instances or deepposekit callbacks.
List of callbacks to apply during training and validation.
epochs: int
Number of epochs to train the model. An epoch is an iteration over the entire dataset,
or for `steps_per_epoch` number of batches
use_multiprocessing: bool, default=False
Whether to use the multiprocessing module when generating batches of data.
n_workers: int
Number of processes to run for generating batches of data.
steps_per_epoch: int or None
Number of batches per epoch. If `None` this is automatically determined
based on the size of the dataset.
"""
if not self.train_model._is_compiled:
warnings.warn(
"""\nAutomatically compiling with default settings: model.compile('adam', 'mse')\n"""
"Call model.compile() manually to use non-default settings.\n"
)
self.train_model.compile("adam", "mse")
train_generator = self.train_generator(
self.n_outputs, batch_size, validation=False, confidence=True
)
validation_generator = self.train_generator(
self.n_outputs, validation_batch_size, validation=True, confidence=True
)
validation_generator = (
None if len(validation_generator) == 0 else validation_generator
)
if validation_generator is None:
warnings.warn(
"No validation set detected, so validation step will not be run and `val_loss` will not be available."
)
activated_callbacks = self.activate_callbacks(callbacks)
self.train_model.fit_generator(
generator=train_generator,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
use_multiprocessing=use_multiprocessing,
workers=n_workers,
callbacks=activated_callbacks,
validation_data=validation_generator,
**kwargs
)
def activate_callbacks(self, callbacks):
activated_callbacks = []
if callbacks is not None:
if len(callbacks) > 0:
for callback in callbacks:
if hasattr(callback, "pass_model"):
callback.pass_model(self)
activated_callbacks.append(callback)
return activated_callbacks
def evaluate(self, batch_size):
if self.train_generator.n_validation > 0:
keypoint_generator = self.train_generator(
n_outputs=1, batch_size=batch_size, validation=True, confidence=False
)
elif self.train_generator.n_validation == 0:
warnings.warn(
"`n_validation` is 0, so the training set will be used for model evaluation"
)
keypoint_generator = self.train_generator(
n_outputs=1, batch_size=batch_size, validation=False, confidence=False
)
y_pred_list = []
confidence_list = []
y_error_list = []
euclidean_list = []
for idx in range(len(keypoint_generator)):
X, y_true = keypoint_generator[idx]
y_pred = self.predict_model.predict_on_batch(X)
confidence_list.append(y_pred[..., -1])
y_pred_coords = y_pred[..., :2]
y_pred_list.append(y_pred_coords)
errors = keypoint_errors(y_true, y_pred_coords)
y_error, euclidean, mae, mse, rmse = errors
y_error_list.append(y_error)
euclidean_list.append(euclidean)
y_pred = np.concatenate(y_pred_list)
confidence = np.concatenate(confidence_list)
y_error = np.concatenate(y_error_list)
euclidean = np.concatenate(euclidean_list)
evaluation_dict = {
"y_pred": y_pred,
"y_error": y_error,
"euclidean": euclidean,
"confidence": confidence,
}
return evaluation_dict
def save(self, path, overwrite=True):
save_model(self, path)
def get_config(self):
config = {}
if self.train_generator:
base_config = self.train_generator.get_config()
return dict(list(base_config.items()) + list(config.items()))
else:
return config
def _build_encoder(self):
encoder_input = self._add_encoder_input()
conv_layers = self._add_conv_layers(encoder_input)
bottleneck = self._add_bottleneck(conv_layers)
self.encoder = Model(encoder_input, bottleneck, name="encoder")
def build_stage2_discriminator():
"""
Create Stage-II discriminator network
"""
input_layer = Input(shape=(256, 256, 3))
x = Conv2D(64, (4, 4),
padding='same',
strides=2,
input_shape=(256, 256, 3),
use_bias=False)(input_layer)
x = LeakyReLU(alpha=0.2)(x)
x = Conv2D(128, (4, 4), padding='same', strides=2, use_bias=False)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = Conv2D(256, (4, 4), padding='same', strides=2, use_bias=False)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = Conv2D(512, (4, 4), padding='same', strides=2, use_bias=False)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = Conv2D(1024, (4, 4), padding='same', strides=2, use_bias=False)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = Conv2D(2048, (4, 4), padding='same', strides=2, use_bias=False)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = Conv2D(1024, (1, 1), padding='same', strides=1, use_bias=False)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = Conv2D(512, (1, 1), padding='same', strides=1, use_bias=False)(x)
x = BatchNormalization()(x)
x2 = Conv2D(128, (1, 1), padding='same', strides=1, use_bias=False)(x)
x2 = BatchNormalization()(x2)
x2 = LeakyReLU(alpha=0.2)(x2)
x2 = Conv2D(128, (3, 3), padding='same', strides=1, use_bias=False)(x2)
x2 = BatchNormalization()(x2)
x2 = LeakyReLU(alpha=0.2)(x2)
x2 = Conv2D(512, (3, 3), padding='same', strides=1, use_bias=False)(x2)
x2 = BatchNormalization()(x2)
added_x = add([x, x2])
added_x = LeakyReLU(alpha=0.2)(added_x)
input_layer2 = Input(shape=(4, 4, 128))
merged_input = concatenate([added_x, input_layer2])
x3 = Conv2D(64 * 8, kernel_size=1, padding="same", strides=1)(merged_input)
x3 = BatchNormalization()(x3)
x3 = LeakyReLU(alpha=0.2)(x3)
x3 = Flatten()(x3)
x3 = Dense(1)(x3)
x3 = Activation('sigmoid')(x3)
stage2_dis = Model(inputs=[input_layer, input_layer2], outputs=[x3])
return stage2_dis
def dropoutResUNet(
num_channel_input=1,
num_channel_output=1,
img_rows=128,
img_cols=128,
y=np.array([-1, 1]), # change to output_range in the future
output_range=None,
lr_init=None,
loss_function=mixedLoss(),
metrics_monitor=[mean_absolute_error, mean_squared_error],
num_poolings=4,
num_conv_per_pooling=3,
num_channel_first=32,
with_bn=True, # don't use for F16 now
with_baseline_concat=True,
with_baseline_addition=-1, # -1 means no
activation_conv='relu', # options: 'elu', 'selu'
activation_output=None, # options: 'tanh', 'sigmoid', 'linear', 'softplus'
kernel_initializer='zeros', # options: 'he_normal'
verbose=1):
# BatchNorm
if with_bn:
def lambda_bn(x):
x = BatchNormalization()(x)
return Activation(activation_conv)(x)
else:
def lambda_bn(x):
return x
# layers For 2D data (e.g. image), "tf" assumes (rows, cols, channels) while "th" assumes (channels, rows, cols).
inputs = Input((img_rows, img_cols, num_channel_input))
if verbose:
print('inputs:', inputs)
'''
Modification descriptioin (Charles 11/16/18)
Added residual blocks to the encoding and decoding sides of the Unet
See if statements within the for loops
Added drop out (can also try SpatialDropout2D)
See drop out layer at end of for loops
'''
'''
Below was modified by Charles
'''
# step1
conv1 = inputs
conv_identity = []
for i in range(num_conv_per_pooling):
if i % 2 == 0 and i != 0:
conv_identity.append(conv1)
conv1 = lambda_bn(conv1)
conv1 = Conv2D(num_channel_first, (3, 3),
padding="same",
activation=activation_conv,
kernel_initializer=kernel_initializer)(conv1)
if (i + 1) % 2 == 0 and i != 1:
conv1 = keras_add([conv_identity[-1], conv1])
pdb.set_trace() # jiahong apr
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
pool1 = SpatialDropout2D(0.5)(pool1)
if verbose:
print('conv1:', conv1, pool1)
# encoder layers with pooling
conv_encoders = [inputs, conv1]
pool_encoders = [inputs, pool1]
conv_identity = []
list_num_features = [num_channel_input, num_channel_first]
for i in range(1, num_poolings):
# step2
conv_encoder = pool_encoders[-1]
num_channel = num_channel_first * (2**(i))
for j in range(num_conv_per_pooling):
if j % 2 == 0 and j != 0:
conv_identity.append(conv_encoder)
conv_encoder = lambda_bn(conv_encoder)
conv_encoder = Conv2D(
num_channel, (3, 3),
padding="same",
activation=activation_conv,
kernel_initializer=kernel_initializer)(conv_encoder)
if (j + 1) % 2 == 0 and j != 1:
conv_encoder = keras_add([conv_identity[-1], conv_encoder])
pdb.set_trace() # jiahong apr
pool_encoder = MaxPooling2D(pool_size=(2, 2))(conv_encoder)
pool_encoder = SpatialDropout2D(0.5)(pool_encoder)
if verbose:
print('encoding#{0}'.format(i), conv_encoder, pool_encoder)
pool_encoders.append(pool_encoder)
conv_encoders.append(conv_encoder)
list_num_features.append(num_channel)
conv_center = Conv2D(list_num_features[-1] * 2, (3, 3),
padding="same",
activation="relu",
kernel_initializer=kernel_initializer,
bias_initializer='zeros')(pool_encoders[-1])
conv_center = Conv2D(list_num_features[-1] * 2, (3, 3),
padding="same",
activation="relu",
kernel_initializer=kernel_initializer,
bias_initializer='zeros')(conv_center)
conv_decoders = [conv_center]
if verbose:
print('centers', conv_center)
# decoder steps
deconv_identity = []
for i in range(1, num_poolings + 1):
attention_gated = create_attention_block_2D(
Conv2DTranspose(list_num_features[-i], (2, 2),
strides=(2, 2),
padding="same",
activation=activation_conv,
kernel_initializer=kernel_initializer)(
conv_decoders[-1]), conv_encoders[-i],
list_num_features[-i])
upsample_decoder = concatenate([
Conv2DTranspose(list_num_features[-i], (2, 2),
strides=(2, 2),
padding="same",
activation=activation_conv,
kernel_initializer=kernel_initializer)(
conv_decoders[-1]), attention_gated
])
conv_decoder = upsample_decoder
for j in range(num_conv_per_pooling):
if j % 2 == 0 and j != 0:
deconv_identity.append(conv_decoder)
conv_decoder = lambda_bn(conv_decoder)
conv_decoder = Conv2D(
list_num_features[-i], (3, 3),
padding="same",
activation=activation_conv,
kernel_initializer=kernel_initializer)(conv_decoder)
if (j + 1) % 2 == 0 and j != 1:
conv_decoder = keras_add([deconv_identity[-1], conv_decoder])
pdb.set_trace() # jiahong apr
conv_decoder = SpatialDropout2D(0.5)(conv_decoder)
conv_decoders.append(conv_decoder)
if verbose:
print('decoding#{0}'.format(i), conv_decoder, upsample_decoder)
'''
Above was modified by Charles
'''
# concatenate with baseline
if with_baseline_concat:
conv_decoder = conv_decoders[-1]
conv_decoder = concatenate([conv_decoder, inputs])
if verbose:
print('residual concatenate:', conv_decoder)
'''
'''
# output layer activation
if output_range is None:
output_range = np.array(y).flatten()
if activation_output is None:
if max(output_range) <= 1 and min(output_range) >= 0:
activation_output = 'sigmoid'
elif max(output_range) <= 1 and min(output_range) >= -1:
activation_output = 'tanh'
else:
activation_output = 'linear'
conv_output = Conv2D(num_channel_output, (1, 1),
padding="same",
activation=activation_output)(conv_decoder)
if verbose:
print('output:', conv_output)
# add baselind channel
if with_baseline_addition > 0:
print('add residual channel {0}#{1}'.format(with_baseline_addition,
num_channel_input // 2))
conv_output = keras_add(
[conv_output, inputs[:, :, :, num_channel_input // 2]])
pdb.set_trace() # jiahong apr
# construct model
model = Model(outputs=conv_output, inputs=inputs)
if verbose:
print('model:', model)
# optimizer and loss
if lr_init is not None:
optimizer = Adam(lr=lr_init)
else:
optimizer = Adam()
model.compile(loss=loss_function,
optimizer=optimizer,
metrics=metrics_monitor)
return model
class ZeroShot(CFs):
def __init__(self, size1=512, size2=128, gru_length=20):
self.backup_path = f"./training/zeroshot__{size1}__{size2}/mdl.ckpt"
self.cp_callback = ModelCheckpoint(
filepath=self.backup_path, save_weights_only=True, verbose=0
)
user_input = Input(shape=(gru_length, 768))
item_input = Input(shape=(768))
self.inputs = [user_input, item_input]
layer1 = Dense(size1, activation="relu")
layer2 = Dense(size2, activation="relu")
self.layers = [layer1, layer2]
self.gru = GRU(size2)
user_present = self.gru(layer2(layer1(user_input)))
item_present = layer2(layer1(item_input))
output = Activation(activation="sigmoid")(
Dot(axes=1)([user_present, item_present])
)
self.model = Model(self.inputs, output, name="ZeroShot")
self.model.compile(
optimizer="adam",
loss=BinaryCrossentropy(),
metrics=[RootMeanSquaredError()],
)
self._update_models()
self._gen_score_layer(size2)
def _update_models(self):
item_function = self.layers[1](self.layers[0](self.inputs[1]))
self.item_model = Model(self.inputs[1], item_function)
print(self.gru.weights)
def load(self):
super().load()
self._update_models()
def fit(self, inputs, label, epochs=10, verbose=1):
super().fit(inputs, label, epochs, verbose)
self._update_models()
def _update_models(self):
item_function = self.layers[1](self.layers[0](self.inputs[1]))
self.item_model = Model(self.inputs[1], item_function)
user_function = self.gru(self.layers[1](self.layers[0](self.inputs[0])))
self.user_model = Model(self.inputs[0], user_function)
def _gen_score_layer(self, size):
input = [Input(shape=(size)), Input(shape=(size))]
output = Activation(activation="sigmoid")(Dot(axes=1)(input))
self.score_layer = Model(input, output)
def predict(self, user_data, item_data):
user_vec = self._embed_user(user_data.reshape(1, 20, 768))
item_vec = self._embed_item(item_data)
user_vec = np.repeat(user_vec, item_vec.shape[0], axis=0)
return self.score_layer.predict([user_vec, item_vec])
def _embed_item(self, item):
return self.item_model.predict(item)
def _embed_user(self, items):
return self.user_model.predict(items)
for layer in pre_trained_model.layers:
layer.trainable = False
# pre_trained_model.summary()
last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output
# Flatten the output layer to 1 dimension
x = layers.Flatten()(last_output)
# Add a fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dense(1024, activation='relu')(x)
# Add a dropout rate of 0.2
x = layers.Dropout(0.3)(x)
# Add a final sigmoid layer for classification
x = layers.Dense(1, activation='sigmoid')(x)
model = Model(pre_trained_model.input, x)
model.compile(optimizer=RMSprop(lr=0.0001),
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit_generator(train_generator,
validation_data=validation_generator,
epochs=20,
verbose=1)
model.save("catsdogs.h5")
def _update_models(self):
item_function = self.layers[1](self.layers[0](self.inputs[1]))
self.item_model = Model(self.inputs[1], item_function)
user_function = self.gru(self.layers[1](self.layers[0](self.inputs[0])))
self.user_model = Model(self.inputs[0], user_function)
train_subjects.index, train_targets,
shuffle=True) # train_subjects.index for selecting training nodes
test_gen = generator.flow(test_subjects.index, test_targets)
# aggregatortype = MaxPoolingAggregator()
graphsage_model = GraphSAGE(layer_sizes=[32, 16, 8, 8],
generator=generator,
activations=["relu", "relu", "relu", "linear"],
bias=True,
dropout=0.0)
x_inp, x_out = graphsage_model.in_out_tensors()
prediction = layers.Dense(units=train_targets.shape[1],
activation="linear")(x_out)
model = Model(inputs=x_inp, outputs=prediction)
##%% ##################################### Model training #######################################################
#%% ############################################################################################################
indices = bf.expandy(batch_size, 1)
def noderankloss(index):
def loss(y_true, y_pred):
# tf.print(tf.gather(y_true, tf.constant(index[:, 0])))
yt = tf.math.sigmoid(
tf.gather(y_true, tf.constant(index[:, 0])) -
tf.gather(y_true, tf.constant(index[:, 1])))
yp = tf.math.sigmoid(