x = Flatten()(vgg.output)
prediction=Dense(len(folders), activation='softmax')(x)
model = Model(inputs=vgg.input, outputs=prediction)
model.summary()
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
batch_size=32
r= model.fit_generator(training_set,epochs = 10, validation_data = test_set,verbose = 1, steps_per_epoch=X_train.shape[0] // batch_size)
# plot the loss
plt.figure(figsize=(10,6))
plt.subplot(1,2,1)
plt.plot(r.history['loss'], label='train loss')
plt.plot(r.history['val_loss'], label='val loss')
plt.legend()
plt.savefig('LossVal_loss')
# plot the accuracy
plt.subplot(1,2,2)
plt.plot(r.history['accuracy'], label='train acc')
plt.plot(r.history['val_accuracy'], label='val acc')
plt.legend()
batch_size=batch_size,
target_size=img_shape)
model = ResNet50(include_top=False, input_shape=(298, 298, 3))
for layer in model.layers:
layer.trainable = False
flat1 = Flatten()(model.layers[-1].output)
class1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)
output = Dense(1, activation='sigmoid')(class1)
model = Model(inputs=model.inputs, outputs=output)
opt = SGD(lr=lr, momentum=0.9)
model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit_generator(train_it,
steps_per_epoch=len(train_it),
epochs=epochs,
verbose=1)
if 'acc' in history.history.keys():
for i in range(1, epochs + 1):
log_metrics('accuracy', float(history.history['acc'][i - 1]), i, i)
log_metrics('loss', float(history.history['loss'][i - 1]), i, i)
print("accuracy=", float(history.history['acc'][i - 1]))
print("loss=", float(history.history['loss'][i - 1]))
else:
for i in range(1, epochs + 1):
log_metrics('accuracy', float(history.history['accuracy'][i - 1]), i,
i)
log_metrics('loss', float(history.history['loss'][i - 1]), i, i)
print("accuracy=", float(history.history['accuracy'][i - 1]))
print("loss=", float(history.history['loss'][i - 1]))
labels[v] = k
train_generator = gen.flow_from_directory(train_path,
target_size=IMAGE_SIZE,
shuffle=True,
batch_size=batch_size)
valid_generator = gen.flow_from_directory(validation_path,
target_size=IMAGE_SIZE,
shuffle=True,
batch_size=batch_size)
r = model.fit_generator(
train_generator,
#validation_data=valid_generator,
epochs=15,
steps_per_epoch=len(image_files) // batch_size,
#validation_steps=len(validation_files)//batch_size
)
def get_confusion_matrix(data_path, N):
print("Generating Confusion Matrix {}".format(N))
predictions = []
targets = []
i = 0
for x, y in gen.flow_from_directory(data_path,
target_size=IMAGE_SIZE,
shuffle=False,
batch_size=batch_size * 2):
i += 1
y_true *= 255
y_pred *= 255
rmean = (y_true[:, :, :, 0] + y_pred[:, :, :, 0]) / 2
r = y_true[:, :, :, 0] - y_pred[:, :, :, 0]
g = y_true[:, :, :, 1] - y_pred[:, :, :, 1]
b = y_true[:, :, :, 2] - y_pred[:, :, :, 2]
return K.mean(K.sqrt((((512+rmean)*r*r)/256) + 4*g*g + (((767-rmean)*b*b)/256)))
val_generator = image_generator(config.batch_size, train_dir)
in_sample_images, out_sample_images = next(val_generator)
class ImageLogger(Callback):
def on_epoch_end(self, epoch, logs):
preds = self.model.predict(in_sample_images)
in_resized = []
for arr in in_sample_images:
# Simple upsampling
in_resized.append(arr.repeat(8, axis=0).repeat(8, axis=1))
wandb.log({
"examples": [wandb.Image(np.concatenate([in_resized[i] * 255, o * 255, out_sample_images[i] * 255], axis=1)) for i, o in enumerate(preds)]
}, commit=False)
opt = Adam(lr=0.0001, beta_1=0.9)
generator.compile(loss='mse', optimizer=opt, metrics=[perceptual_distance])
generator.fit_generator(image_generator(config.batch_size, train_dir),
steps_per_epoch=config.steps_per_epoch,
epochs=config.num_epochs, callbacks=[
ImageLogger(), WandbCallback()],
validation_steps=config.val_steps_per_epoch,
validation_data=val_generator)
class IIC():
def __init__(self, args, backbone):
self.args = args
self.backbone = backbone
self._model = None
self.train_gen = DataGenerator(args, siamese=True)
self.n_labels = self.train_gen.n_labels
self.build_model()
self.load_eval_dataset()
self.accuracy = 0
# build the n_heads of the IIC model
def build_model(self):
inputs = Input(shape=self.train_gen.input_shape)
x = self.backbone(inputs)
x = Flatten()(x)
outputs = []
for i in range(self.args.heads):
name = "head%d" % i
outputs.append(
Dense(self.n_labels, activation='softmax', name=name)(x))
self._model = Model(inputs, outputs, name='encoder')
optimizer = Adam(lr=1e-3)
self._model.compile(optimizer=optimizer, loss=self.loss)
self._model.summary()
# MI loss
def loss(self, y_true, y_pred):
size = self.args.batch_size
n_labels = y_pred.shape[-1]
# lower half is Z
Z = y_pred[0:size, :]
Z = K.expand_dims(Z, axis=2)
# upper half is Zbar
Zbar = y_pred[size:y_pred.shape[0], :]
Zbar = K.expand_dims(Zbar, axis=1)
# compute joint distribution
P = K.batch_dot(Z, Zbar)
P = K.sum(P, axis=0)
# enforce symmetric joint distribution
P = (P + K.transpose(P)) / 2.0
P = P / K.sum(P)
# marginal distributions
Pi = K.expand_dims(K.sum(P, axis=1), axis=1)
Pj = K.expand_dims(K.sum(P, axis=0), axis=0)
Pi = K.repeat_elements(Pi, rep=n_labels, axis=1)
Pj = K.repeat_elements(Pj, rep=n_labels, axis=0)
P = K.clip(P, K.epsilon(), np.finfo(float).max)
Pi = K.clip(Pi, K.epsilon(), np.finfo(float).max)
Pj = K.clip(Pj, K.epsilon(), np.finfo(float).max)
# negative MI loss
neg_mi = K.sum((P * (K.log(Pi) + K.log(Pj) - K.log(P))))
# each head contribute 1/n_heads to the total loss
return neg_mi / self.args.heads
# train the model
def train(self):
accuracy = AccuracyCallback(self)
lr_scheduler = LearningRateScheduler(lr_schedule, verbose=1)
callbacks = [accuracy, lr_scheduler]
self._model.fit_generator(generator=self.train_gen,
use_multiprocessing=True,
epochs=self.args.epochs,
callbacks=callbacks,
workers=4,
shuffle=True)
# pre-load test data for evaluation
def load_eval_dataset(self):
(_, _), (x_test, self.y_test) = self.args.dataset.load_data()
image_size = x_test.shape[1]
x_test = np.reshape(x_test, [-1, image_size, image_size, 1])
x_test = x_test.astype('float32') / 255
x_eval = np.zeros([x_test.shape[0], *self.train_gen.input_shape])
for i in range(x_eval.shape[0]):
x_eval[i] = center_crop(x_test[i])
self.x_test = x_eval
# reload model weights for evaluation
def load_weights(self):
if self.args.restore_weights is None:
raise ValueError("Must load model weights for evaluation")
if self.args.restore_weights:
folder = "weights"
os.makedirs(folder, exist_ok=True)
path = os.path.join(folder, self.args.restore_weights)
print("Loading weights... ", path)
self._model.load_weights(path)
# evaluate the accuracy of the current model weights
def eval(self):
y_pred = self._model.predict(self.x_test)
print("")
# accuracy per head
for head in range(self.args.heads):
if self.args.heads == 1:
y_head = y_pred
else:
y_head = y_pred[head]
y_head = np.argmax(y_head, axis=1)
accuracy = unsupervised_labels(list(self.y_test), list(y_head),
self.n_labels, self.n_labels)
info = "Head %d accuracy: %0.2f%%"
if self.accuracy > 0:
info += ", Old best accuracy: %0.2f%%"
data = (head, accuracy, self.accuracy)
else:
data = (head, accuracy)
print(info % data)
# if accuracy improves during training,
# save the model weights on a file
if accuracy > self.accuracy \
and self.args.save_weights is not None:
self.accuracy = accuracy
folder = self.args.save_dir
os.makedirs(folder, exist_ok=True)
path = os.path.join(folder, self.args.save_weights)
print("Saving weights... ", path)
self._model.save_weights(path)
@property
def model(self):
return self._model
caption_model = Model(inputs=[inputs1, inputs2], outputs=outputs)
caption_model.layers[2].set_weights([embedding_matrix])
caption_model.layers[2].trainable = False
caption_model.compile(loss='categorical_crossentropy', optimizer='adam')
number_pics_per_bath = 3
steps = len(train_descriptions)//number_pics_per_bath
model_path = os.path.join(root_captioning,"data",f'caption-model-coco.hdf5')
if not os.path.exists(model_path):
for i in tqdm(range(EPOCHS*2)):
generator = data_generator(train_descriptions, encoding_train, wordtoidx, max_length, number_pics_per_bath)
caption_model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
caption_model.optimizer.lr = 1e-4
number_pics_per_bath = 6
steps = len(train_descriptions)//number_pics_per_bath
for i in range(EPOCHS):
generator = data_generator(train_descriptions, encoding_train, wordtoidx, max_length, number_pics_per_bath)
caption_model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
caption_model.save_weights(model_path)
print(f"\Training took: {hms_string(time()-start)}")
else:
caption_model.load_weights(model_path)
def generateCaption(photo):
def main():
directory = 'img' # 画像が保存されているフォルダ
df_train = pd.read_csv('train.csv') # 学習データの情報がかかれたDataFrame
df_validation = pd.read_csv('val.csv') # 検証データの情報がかかれたDataFrame
df_test = pd.read_csv('test.csv') # テストデータの情報がかかれたDataFrame
label_list = ['AMD', 'DR_DM', 'Gla', 'MH', 'Normal', 'RD', 'RP', 'RVO'] # ラベル名
image_size = (224, 224) # 入力画像サイズ
classes = len(label_list) # 分類クラス数
batch_size = 32 # バッチサイズ
epochs = 300 # エポック数
loss = 'categorical_crossentropy' # 損失関数
optimizer = Adam(lr=0.00001, amsgrad=True) # 最適化関数
metrics = 'accuracy' # 評価方法
# ImageDataGenerator画像増幅のパラメータ
aug_params = {'rotation_range': 5,
'width_shift_range': 0.05,
'height_shift_range': 0.05,
'shear_range': 0.1,
'zoom_range': 0.05,
'horizontal_flip': True,
'vertical_flip': True}
# val_lossが最小になったときのみmodelを保存
mc_cb = ModelCheckpoint('model_weights.h5',
monitor='val_loss', verbose=1,
save_best_only=True, mode='min')
# 学習が停滞したとき、学習率を0.2倍に
rl_cb = ReduceLROnPlateau(monitor='loss', factor=0.2, patience=3,
verbose=1, mode='auto',
min_delta=0.0001, cooldown=0, min_lr=0)
# 学習が進まなくなったら、強制的に学習終了
es_cb = EarlyStopping(monitor='loss', min_delta=0,
patience=5, verbose=1, mode='auto')
# データの数に合わせて損失の重みを調整
weight_balanced = {}
for i, label in enumerate(label_list):
weight_balanced[i] = (df_train['label'] == label).sum()
max_count = max(weight_balanced.values())
for label in weight_balanced:
weight_balanced[label] = max_count / weight_balanced[label]
print(weight_balanced)
# ジェネレータの生成
## 学習データのジェネレータ
datagen = ImageDataGenerator(rescale=1./255, **aug_params)
train_generator = datagen.flow_from_dataframe(
dataframe=df_train, directory=directory,
x_col='filename', y_col='label',
target_size=image_size, class_mode='categorical',
classes=label_list,
batch_size=batch_size)
step_size_train = train_generator.n // train_generator.batch_size
## 検証データのジェネレータ
datagen = ImageDataGenerator(rescale=1./255)
validation_generator = datagen.flow_from_dataframe(
dataframe=df_validation, directory=directory,
x_col='filename', y_col='label',
target_size=image_size, class_mode='categorical',
classes=label_list,
batch_size=batch_size)
step_size_validation = validation_generator.n // validation_generator.batch_size
# ネットワーク構築
base_model = EfficientNetB0(include_top=False, weights='imagenet', pooling='avg',
input_shape=(image_size[0], image_size[1], 3),
backend=tf.keras.backend, layers=tf.keras.layers,
models=tf.keras.models, utils=tf.keras.utils)
x = Dense(256, kernel_initializer='he_normal')(base_model.output)
x = Dense(classes, kernel_initializer='he_normal')(x)
outputs = Activation('softmax')(x)
model = Model(inputs=base_model.inputs, outputs=outputs)
model.summary()
model.compile(loss=loss, optimizer=optimizer, metrics=[metrics])
# 学習
history = model.fit_generator(
train_generator, steps_per_epoch=step_size_train,
epochs=epochs, verbose=1, callbacks=[mc_cb, rl_cb, es_cb],
validation_data=validation_generator,
validation_steps=step_size_validation,
class_weight=weight_balanced,
workers=3)
# 学習曲線の保存
plot_history(history)
# テストデータの評価
## 学習済み重みの読み込み
model.load_weights('model_weights.h5')
## 推論
X = df_test['filename'].values
y_true = list(map(lambda x: label_list.index(x), df_test['label'].values))
y_pred = []
for file in tqdm(X, desc='pred'):
# 学習時と同じ条件になるように画像をリサイズ&変換
img = Image.open(f'{directory}/{file}')
img = img.resize(image_size)
img = np.array(img, dtype=np.float32)
img *= 1./255
img = np.expand_dims(img, axis=0)
y_pred.append(np.argmax(model.predict(img)[0]))
## 評価
print(classification_report(y_true, y_pred, target_names=label_list))
include_top=False,
weights='imagenet')
w = base_model.output
w = Flatten()(w)
w = Dense(256, activation="relu")(w)
w = Dense(128, activation="relu")(w)
output = Dense(17, activation="sigmoid")(w)
# Compile the model for execution. Losses and optimizers
# can be anything here, since we don’t train the model.
model = Model(inputs=[base_model.inputs[0]], outputs=[output])
model.layers[-6].trainable = True
model.layers[-7].trainable = True
model.layers[-8].trainable = True
#train NN
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=val_generator,
validation_steps=STEP_SIZE_VALID,
epochs=EpochSize,
class_weight=weights)
model.save('EfficientNetB5_Comp.h5')
print(layer.name, layer.trainable)
# compile the model
print("----------Compiling model----------")
optim = Adam(lr=init_LR, decay=init_LR / Epochs)
model.compile(loss="binary_crossentropy",
optimizer=optim,
metrics=["accuracy"])
# train the head of the network
print("----------Training head----------")
trainHead = model.fit_generator(traindataAugm.flow(train_X,
train_Y,
batch_size=BS),
steps_per_epoch=len(train_X) // BS,
validation_data=(val_X, val_Y),
validation_steps=len(val_X) // BS,
epochs=Epochs)
# make predictions on the testing set
print("----------Evaluating test set----------")
testpred = model.predict(test_X, batch_size=BS)
# set label with corresponding largest predicted probability
testpred_a = (testpred[:, 1] > 0.5) * 1
#Classification report #########################
print(
classification_report(test_Y.argmax(axis=1),
# 設定凍結與要進行訓練的網路層
net_final = Model(inputs=net.input, outputs=output_layer)
for layer in net_final.layers[:FREEZE_LAYERS]:
layer.trainable = False
for layer in net_final.layers[FREEZE_LAYERS:]:
layer.trainable = True
# 使用 Adam optimizer，以較低的 learning rate 進行 fine-tuning
net_final.compile(optimizer=Adam(lr=1e-5),
loss='categorical_crossentropy', metrics=['accuracy'])
logdir = os.path.join(
"logs", datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)
# 輸出整個網路結構
print(net_final.summary())
# 訓練模型
net_final.fit_generator(train_batches,
steps_per_epoch=train_batches.samples // BATCH_SIZE,
validation_data=valid_batches,
validation_steps=valid_batches.samples // BATCH_SIZE,
epochs=NUM_EPOCHS,
callbacks=[tensorboard_callback])
# 儲存訓練好的模型
net_final.save(WEIGHTS_FINAL)
# 如果需要show出tensorboard的圖 使用下面這行
# %tensorboard --logdir logs
else:
file_name = "dense"
checkpointer = ModelCheckpoint(
"../data/models/" + file_name + "_ms_transfer_alternative_init." +
"{epoch:02d}-{val_categorical_accuracy:.3f}." + "hdf5",
monitor='val_categorical_accuracy',
verbose=1,
save_best_only=True,
mode='max')
earlystopper = EarlyStopping(monitor='val_categorical_accuracy',
patience=10,
mode='max',
restore_best_weights=True)
history = model.fit_generator(train_generator,
steps_per_epoch=1000,
epochs=10000,
callbacks=[checkpointer, earlystopper],
validation_data=validation_generator,
validation_steps=500)
initial_epoch = len(history.history['loss']) + 1
# at this point, the top layers are well trained and we can start fine-tuning
# convolutional layers. We will freeze the bottom N layers
# and train the remaining top layers.
# let's visualize layer names and layer indices to see how many layers
# we should freeze:
names = []
for i, layer in enumerate(model.layers):
names.append([i, layer.name, layer.trainable])
print(names)
Batch_size = 128
train_generator = gen.flow_from_directory(train_path,
shuffle=True,
target_size=img_size,
batch_size=Batch_size)
valid_generator = gen.flow_from_directory(validation_path,
target_size=img_size,
batch_size=Batch_size)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# adding early stopping
from tensorflow.keras.callbacks import EarlyStopping
early_stop = EarlyStopping(monitor='val_loss', patience=2)
#fit model
results = model.fit_generator(train_generator,
validation_data=valid_generator,
epochs=25,
callbacks=[early_stop])
#accuracy= approx 90%
# model.save('rock_paper_scissor.h5')
optimizer = Adam(0.001)
combine.compile(loss='categorical_crossentropy', optimizer=optimizer,
metrics=['categorical_accuracy'])
train_loader = DataGenerator(way=train_way, query=train_query, shot=shot, num_batch=1000)
val_loader = DataGenerator(data_type='val',way=val_way, shot=shot)
test_loader = DataGenerator(data_type='test',way=val_way, shot=shot, num_batch=1000)
save_conv = SaveConv()
reduce_lr = cb.ReduceLROnPlateau(monitor='val_loss', factor=0.4,patience=2, min_lr=1e-8)
lr_sched = cb.LearningRateScheduler(scheduler)
tensorboard = cb.TensorBoard()
combine.fit_generator(train_loader,epochs=50,validation_data=val_loader,
use_multiprocessing=True, workers=4, shuffle=False,
callbacks=[save_conv, lr_sched, tensorboard])
combine.evaluate(test_loader)
save_model(conv, "model/miniimage_conv_{epoch}_{shot}_{val_way}")
combine.evaluate(test_loader)
# images, labels = zip(*list(loader('python/images_background')))
# images = np.expand_dims(images, axis=-1)
# images = np.repeat(images, repeats=3, axis=-1)
# print(images.shape)
# main_labels, sub_labels= [x[0] for x in labels], [x[1] for x in labels]
# encoder = LabelBinarizer()
# enc_main_labels = encoder.fit_transform(main_labels)
# output_num = len(np.unique(main_labels))
figPath = os.path.join(model_dir, 'checkpoints', 'progress', 'train.png')
jsonPath = os.path.join(model_dir, 'checkpoints', 'progress', 'train.json')
train_monitor = TrainingMonitor(figPath,
jsonPath=jsonPath,
startAt=init_epoch_train)
# TypeError: Object of type float32 is not JSON serializable
# train_monitor = TrainingMonitor(figPath)
callbacks_list = [early_stop, reduce_lr, checkpoint, train_monitor]
history = model.fit_generator(train_generator,
steps_per_epoch=train_steps,
epochs=train_epochs,
validation_data=validation_generator,
validation_steps=validation_steps,
class_weight=class_weights,
initial_epoch=init_epoch_train,
max_queue_size=15,
workers=8,
callbacks=callbacks_list)
plot_history(history,
save_fig=True,
save_path=os.path.join('data', 'models', 'bottleneck.png'))
print("\n")
bn_end_time = datetime.now()
print("[Info] Model Bottlenecking completed at: {}".format(bn_end_time))
bn_duration = bn_end_time - bn_start_time
class MINE:
def __init__(self, args, backbone):
"""Contains the encoder, SimpleMINE, and linear
classifier models, the loss function,
loading of datasets, train and evaluation routines
to implement MINE unsupervised clustering via mutual
information maximization
Arguments:
args : Command line arguments to indicate choice
of batch size, folder to save
weights file, weights file name, etc
backbone (Model): MINE Encoder backbone (eg VGG)
"""
self.args = args
self.latent_dim = args.latent_dim
self.backbone = backbone
self._model = None
self._encoder = None
self.train_gen = DataGenerator(args, siamese=True, mine=True)
self.n_labels = self.train_gen.n_labels
self.build_model()
self.accuracy = 0
def build_model(self):
"""Build the MINE model unsupervised classifier
"""
inputs = Input(shape=self.train_gen.input_shape, name="x")
x = self.backbone(inputs)
x = Flatten()(x)
y = Dense(self.latent_dim, activation='linear', name="encoded_x")(x)
# encoder is based on backbone (eg VGG)
# feature extractor
self._encoder = Model(inputs, y, name="encoder")
# the SimpleMINE in bivariate Gaussian is used
# as T(x,y) function in MINE (Algorithm 13.7.1)
self._mine = SimpleMINE(self.args,
input_dim=self.latent_dim,
hidden_units=1024,
output_dim=1)
inputs1 = Input(shape=self.train_gen.input_shape, name="x")
inputs2 = Input(shape=self.train_gen.input_shape, name="y")
x1 = self._encoder(inputs1)
x2 = self._encoder(inputs2)
outputs = self._mine.model([x1, x2])
# the model computes the MI between
# inputs1 and 2 (x and y)
self._model = Model([inputs1, inputs2], outputs, name='encoder')
optimizer = Adam(lr=1e-3)
self._model.compile(optimizer=optimizer, loss=self.mi_loss)
self._model.summary()
self.load_eval_dataset()
self._classifier = LinearClassifier(\
latent_dim=self.latent_dim)
def mi_loss(self, y_true, y_pred):
""" MINE loss function
Arguments:
y_true (tensor): Not used since this is
unsupervised learning
y_pred (tensor): stack of predictions for
joint T(x,y) and marginal T(x,y)
"""
size = self.args.batch_size
# lower half is pred for joint dist
pred_xy = y_pred[0:size, :]
# upper half is pred for marginal dist
pred_x_y = y_pred[size:y_pred.shape[0], :]
loss = K.mean(K.exp(pred_x_y))
loss = K.clip(loss, K.epsilon(), np.finfo(float).max)
loss = K.mean(pred_xy) - K.log(loss)
return -loss
def train(self):
"""Train MINE to estimate MI between
X and Y (eg MNIST image and its transformed
version)
"""
accuracy = AccuracyCallback(self)
lr_scheduler = LearningRateScheduler(lr_schedule, verbose=1)
callbacks = [accuracy, lr_scheduler]
self._model.fit_generator(generator=self.train_gen,
use_multiprocessing=True,
epochs=self.args.epochs,
callbacks=callbacks,
workers=4,
shuffle=True)
def load_eval_dataset(self):
"""Pre-load test data for evaluation
"""
(_, _), (x_test, self.y_test) = \
self.args.dataset.load_data()
image_size = x_test.shape[1]
x_test = np.reshape(x_test, [-1, image_size, image_size, 1])
x_test = x_test.astype('float32') / 255
x_eval = np.zeros([x_test.shape[0], *self.train_gen.input_shape])
for i in range(x_eval.shape[0]):
x_eval[i] = center_crop(x_test[i])
self.y_test = to_categorical(self.y_test)
self.x_test = x_eval
def load_weights(self):
"""Reload model weights for evaluation
"""
if self.args.restore_weights is None:
error_msg = "Must load model weights for evaluation"
raise ValueError(error_msg)
if self.args.restore_weights:
folder = "weights"
os.makedirs(folder, exist_ok=True)
path = os.path.join(folder, self.args.restore_weights)
print("Loading weights... ", path)
self._model.load_weights(path)
def eval(self):
"""Evaluate the accuracy of the current model weights
"""
# generate clustering predictions fr test data
y_pred = self._encoder.predict(self.x_test)
# train a linear classifier
# input: clustered data
# output: ground truth labels
self._classifier.train(y_pred, self.y_test)
accuracy = self._classifier.eval(y_pred, self.y_test)
info = "Accuracy: %0.2f%%"
if self.accuracy > 0:
info += ", Old best accuracy: %0.2f%%"
data = (accuracy, self.accuracy)
else:
data = (accuracy)
print(info % data)
# if accuracy improves during training,
# save the model weights on a file
if accuracy > self.accuracy \
and self.args.save_weights is not None:
folder = self.args.save_dir
os.makedirs(folder, exist_ok=True)
args = (self.latent_dim, self.args.save_weights)
filename = "%d-dim-%s" % args
path = os.path.join(folder, filename)
print("Saving weights... ", path)
self._model.save_weights(path)
if accuracy > self.accuracy:
self.accuracy = accuracy
@property
def model(self):
return self._model
@property
def encoder(self):
return self._encoder
@property
def classifier(self):
return self._classifier
subset='validation')
import tensorflow.keras.layers as layers
from tensorflow.keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Dropout, Reshape, Concatenate, LeakyReLU
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.applications import MobileNetV2
x = Input(shape=(256, 256, 3))
#ImageNet weights
model_mn = MobileNetV2(input_shape=(256, 256, 3), alpha=1.3, include_top=False)
#Use the generated model
output_mn = model_mn(x)
#Add the fully-connected layers
y = Flatten()(output_mn)
y = Dense(1000, activation='relu')(y)
y = Dense(1000, activation='relu')(y)
y = Dropout(0.5)(y)
y = Dense(1, activation='sigmoid')(y)
model = Model(inputs=x, outputs=y)
model.summary()
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit_generator(training_images,
validation_data=validation_images,
epochs=10)
mode='auto')
es = EarlyStopping(mode='min', monitor='val_loss', min_delta=0.001, verbose=1)
adam = Adam(lr=1e-5, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
print("Traning Model...")
history = model.fit_generator(train_data_gen,
epochs=epochs,
verbose=1,
validation_data=valid_data_gen,
callbacks=[checkpoint, es],
steps_per_epoch=len(train_data_gen),
validation_steps=len(valid_data_gen))
# test
y_pred = model.predict(test_data_gen)
y_pred_labels = np.argmax(y_pred, axis=1)
print('VGG16,acc on test:\t',
accuracy_score(test_data_gen.classes, y_pred_labels))
confusion_matrix = metrics.confusion_matrix(y_true=test_data_gen.classes,
y_pred=y_pred_labels)
print(confusion_matrix)
save_best_only=True,
mode='max')
reduce_lr = ReduceLROnPlateau(monitor='val_top_3_accuracy',
factor=0.5,
patience=2,
verbose=1,
mode='max',
min_lr=0.00001)
callbacks_list = [checkpoint, reduce_lr]
history = model.fit_generator(train_batches,
steps_per_epoch=train_steps,
class_weight=class_weights,
validation_data=valid_batches,
validation_steps=val_steps,
epochs=30,
verbose=0,
callbacks=callbacks_list)
# Now we need to conver the model.h5 model to a tflite model so that it is
# compatible with the tensorflow android app.
new_model = tf.keras.models.load_model(filepath="model.h5",
custom_objects={
'top_2_accuracy': top_2_accuracy,
'top_3_accuracy': top_3_accuracy
})
tflite_converter = tf.lite.TFLiteConverter.from_keras_model(new_model)
tflite_model = tflite_converter.convert()
#Plot the accuracy...............
plt.plot(r.history['accuracy'], label='accuracy')
plt.plot(r.history['val_accuracy'], label='val_accuracy')
plt.legend()
plt.show()
#Fit the model with data augmentation
#Remember:: If you run this after first fit, it will continue training where it left off.
batch_size = 32
data_generator = tf.keras.preprocessing.image.ImageDataGenerator(
width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)
train_generator = data_generator.flow(x_train, y_train, batch_size)
steps_per_epochs = x_train.shape[0] // batch_size
r = model.fit_generator(train_generator,
validation_data=(x_test, y_test),
steps_per_epoch=steps_per_epochs,
epochs=7)
#Plot the loss...........
plt.plot(r.history['loss'], label='loss')
plt.plot(r.history['val_loss'], label='val_loss')
plt.legend()
plt.show()
#Plot the accuracy............
plt.plot(r.history['accuracy'], label='accuracy')
plt.plot(r.history['val_accuracy'], label='val_accuracy')
plt.legend()
plt.show()
es = EarlyStopping(monitor='val_loss', patience=15, mode='min')
file_path = 'c:/data/modelcheckpoint/lotte_last5.hdf5'
mc = ModelCheckpoint(file_path,
monitor='val_loss',
save_best_only=True,
mode='min',
verbose=1)
rl = ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
patience=5,
verbose=1,
mode='min')
model.fit_generator(xy_train,
steps_per_epoch=(xy_train.samples / xy_train.batch_size),
epochs=1000,
validation_data=xy_val,
validation_steps=(xy_val.samples / xy_val.batch_size),
callbacks=[es, mc, rl])
from tensorflow.keras.models import load_model
model = load_model('c:/data/modelcheckpoint/lotte_last5.hdf5')
for i in range(72000):
image = Image.open(f'../data/lotte/test/test/{i}.jpg')
image.save('../data/lotte/test_new/test_new/{0:05}.jpg'.format(i))
test_data = test_datagen.flow_from_directory('../data/lotte/test_new',
target_size=(img_size, img_size),
batch_size=batch,
class_mode=None,
shuffle=False)
# Use GPU
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
base_model = ResNet50(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x)
predictions = Dense(n_classes, activation = 'softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
for layer in base_model.layers:
layer.trainable = False
# define optimizers
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
# training
model.fit_generator(generator=train_generator,
validation_data=test_generator,
use_multiprocessing=True,
workers=Config['num_workers'],
)
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=False)
model_final.compile(loss='binary_crossentropy',
optimizer=optim,
metrics=['accuracy', keras_metrics.fbeta_score])
#%%
history = model_final.fit_generator(
image_generator(image_list,
labels,
batch_size=16,
selection_indices=train_indices),
steps_per_epoch=len(train_indices) / 16,
epochs=10,
validation_data=image_generator(image_list,
labels,
batch_size=16,
selection_indices=val_indices),
validation_steps=len(val_indices) / 16)
#%%
# model_final.summary()
print(len(model_final.layers[-3:]))
# for layer in model_final.layers[:-3]:
# layer.trainable = False
#%%
#%%
'ctc_loss': lambda y_true, y_pred: y_pred
},
optimizer="adam")
complete_model.summary()
def decode_examples(images, labels):
"""Transform our output for example logging"""
return complete_model.predict(format_batch_ctc(images, labels)[0])[1]
# Initialize generators and train
train = Generator(dataset.x_train,
dataset.y_train,
batch_size=32,
format_fn=format_batch_ctc)
test = Generator(dataset.x_test,
dataset.y_test,
batch_size=32,
format_fn=format_batch_ctc)
complete_model.fit_generator(
train,
epochs=75,
callbacks=[
ExampleLogger(dataset, decode_examples),
ModelCheckpoint("best-ctc.h5", save_best_only=True)
],
validation_data=test,
)
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(
accuracy, misclass))
plt.show()
save_models = tf.keras.models.load_model('saved_model.h5', compile=False)
save_models.compile(Adam(lr=0.0001),
loss='categorical_crossentropy',
metrics=['accuracy'])
trained_model = model.fit_generator(train_batch,
validation_data=test_batch,
epochs=30)
loss, accuracy = save_models.evaluate(test_batch)
print(f'loss: {loss} and accuracy: {accuracy*100}')
# new_model = model.save("saved_model.h5")
test_labels = test_batch.classes # this line shows classes and store into test_labels
print(test_labels) # shows classes
print(test_batch.class_indices
) # show class indices like 0 for car 1 for cat 2 for man
predictions = save_models.predict_generator(test_batch)
cm = confusion_matrix(test_labels, predictions.argmax(axis=1))
print(f'model input : {model.input}, model input_names : {model.input_names} ')
print(
f'model output : {model.output}, model output_names : {model.output_names} '
)
# use generator
training_generator = DataProcessor(train_list)
validation_generator = DataProcessor(valid_list)
# dataIt = iter(training_generator)
# for i in range(2):
# x, y = next(dataIt)
# print(x[0])
''' training '''
early = EarlyStopping(monitor='val_loss',
mode='min',
patience=3,
min_delta=0.01)
model.fit_generator(training_generator,
validation_data=validation_generator,
epochs=20,
callbacks=[early],
use_multiprocessing=True,
workers=2)
# save weights
if not os.path.exists('./weights'):
os.makedirs('./weights')
model.save_weights('./weights/mnist.h5')
''' test (eval) '''
test_files = []
for root, _, files in os.walk('./data/testing'):
for fname in files:
filepath = os.path.join(root, fname)
test_files.append(filepath)
input_shape=(64, 64, 3))
resnet.trainable = False
x = resnet.output
x = GlobalAveragePooling2D()(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.2)(x)
predictions = Dense(n_classes,
kernel_regularizer=regularizers.l2(0.005),
activation='softmax')(x)
model = Model(inputs=resnet.input, outputs=predictions)
model.compile(optimizer='SGD',
loss='binary_crossentropy',
metrics=['accuracy'])
history = model.fit_generator(
train_generator,
steps_per_epoch=150,
epochs=350,
verbose=1,
)
model.save('papaya.hdf5')
plt.title('epochs vs loss')
plt.plot(history.history['loss'])
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train_loss', 'validation_loss'], loc='best')
plt.show()
center_loss = get_center_loss(0.5, num_classes, feature_dim)
adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
custom_model.compile(loss={
'prediction': 'sparse_categorical_crossentropy',
'features': center_loss
},
optimizer=adam,
metrics={'prediction': 'accuracy'})
pdb.set_trace()
##############################################################################
history = custom_model.fit_generator(
train_generator,
steps_per_epoch=(num_train_samples) // batch_size,
validation_data=validation_generator,
validation_steps=(num_validation_samples) // batch_size,
epochs=25)
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['prediction_accuracy'])
plt.plot(history.history['val_prediction_accuracy'])
plt.title('Model accuracy of prediction')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
plt.plot(history.history['features_loss'])
plt.plot(history.history['val_features_loss'])
plt.title('model loss of features')
min_delta=0.00008,
mode='max',
cooldown=3,
verbose=1),
ModelCheckpoint("./models/lstm_xception_full1.h5",
monitor='val_top_3_accuracy',
mode='max',
save_best_only=True,
verbose=1)
]
hists = []
hist = model.fit_generator(train_datagen,
steps_per_epoch=STEPS,
epochs=EPOCHS,
verbose=1,
validation_data=val_datagen,
validation_steps=500,
callbacks=callbacks)
hists.append(hist)
# Validation
print("Running validation")
df = pd.read_csv(os.path.join(DP_DIR, 'train_k{}.csv.gz'.format(NCSVS - 1)),
nrows=34000)
for i in range(10):
valid_df = df.loc[i * 3400:(i + 1) * 3400, :].copy()
x_valid, x2 = df_to_image_array_xd(valid_df, SIZE)
y_valid = keras.utils.to_categorical(valid_df.y, num_classes=NCATS)
print(x_valid.shape, y_valid.shape)
headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(4, 4))(headModel)
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(64, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(3, activation="softmax")(headModel)
# place the head FC model on top of the base model (this will become
# the actual model we will train)
model = Model(inputs=baseModel.input, outputs=headModel)
# loop over all layers in the base model and freeze them so they will
# *not* be updated during the first training process
for layer in baseModel.layers:
layer.trainable = False
# In[ ]:
# compile our model
print("[INFO] compiling model...")
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])
# train the head of the network
print("[INFO] training head...")
H = model.fit_generator(trainAug.flow(trainX, trainY, batch_size=BS),
steps_per_epoch=len(trainX) // BS,
validation_data=(testX, testY),
validation_steps=len(testX) // BS,
epochs=EPOCHS)
# In[ ]:
loss="categorical_crossentropy",
metrics=['accuracy'],
)
# train the model
# define callback function
early_stopping = EarlyStopping(monitor='val_loss',
patience=10,
)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
)
model.fit_generator(train_generator,
steps_per_epoch=train_image_numbers//batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_steps,
callbacks=[early_stopping, reduce_lr],
)
# test the model
test_metrics = model.evaluate_generator(test_generator,
steps=1,
)
for i in zip(model.metrics_names, test_metrics):
print(i)
# save the model
model.save(model_filepath)
