I am trying to learn how to use multi-models input with flow_from_directory, but there is something I can't figure out. thanks for your help.
The way I understand is that we supply the fit_generator with the two_image_generator and the and the fit method will infer the labels.... what I am missing...
def two_image_generator(generator,
directory,
batch_size,
shuffle = False,
img_size1 = (224,224),
img_size2 = (299,299)):
gen1 = generator.flow_from_directory(
# This is the target directory
directory,
# All images will be resized to target height and width.
target_size=img_size1,
batch_size=batch_size,
# Since we use categorical_crossentropy loss, we need categorical labels
class_mode='categorical',
shuffle = shuffle,
seed = 1)
gen2 = generator.flow_from_directory(
# This is the target directory
directory,
# All images will be resized to target height and width.
target_size=img_size2,
batch_size=batch_size,
# Since we use categorical_crossentropy loss, we need categorical labels
class_mode='categorical',
shuffle = shuffle,
seed = 1)
while True:
X1i = gen1.next()
X2i = gen2.next()
if y_col:
yield [X1i[0], X2i[0]], X1i[1] #X1i[1] is the label
else:
yield [X1i, X2i]
#add data_augmentation
train_aug_datagen = ImageDataGenerator(
rotation_range = 20,
shear_range = 0.1,
zoom_range = 0.2,
width_shift_range = 0.1,
height_shift_range = 0.1,
horizontal_flip = True
)
train_generator = two_image_generator(train_aug_datagen,
train_dir,
batch_size = batch_size,
shuffle = True)
validation_datagen = ImageDataGenerator()
validation_generator = two_image_generator(validation_datagen,
validation_dir,
batch_size = batch_size,
shuffle = True)
def create_base_model(MODEL, img_size, lambda_fun = None):
inp = Input(shape = (img_size[0], img_size[1], 3))
x = inp
if lambda_fun:
x = Lambda(lambda_fun)(x)
base_model = MODEL(input_tensor = x, weights = 'imagenet',
include_top = False, pooling = 'avg')
model = Model(inp, base_model.output)
return model
#define vgg + resnet50 + densenet
model1 = create_base_model(vgg16.VGG16, (224, 224), vgg16.preprocess_input)
model2 = create_base_model(resnet50.ResNet50, (224, 224), resnet50.preprocess_input)
model3 = create_base_model(inception_v3.InceptionV3, (299, 299), inception_v3.preprocess_input)
model1.trainable = False
model2.trainable = False
model3.trainable = False
inpA = Input(shape = (224, 224, 3))
inpB = Input(shape = (299, 299, 3))
out1 = model1(inpA)
out2 = model2(inpA)
out3 = model3(inpB)
x = Concatenate()([out1, out2, out3])
x = Dropout(0.2)(x)
x = Dense(2, activation='softmax')(x)
model = Model([inpA, inpB], x)
############################################################################
trained_models_path = './models/VggFace_best_model'
model_names = trained_models_path + '_epoch_{epoch:02d}_val_acc_{val_accuracy:.4f}.hdf5'
checkpoint = ModelCheckpoint(model_names, 'val_accuracy', verbose=1, save_best_only=True)
############################################################################
early = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, mode='auto')
callbacks = [checkpoint,early]
history = model.fit_generator(train_generator,
steps_per_epoch= NUM_TRAIN //batch_size,
epochs=100,
validation_data=validation_generator,
validation_steps= NUM_TEST //batch_size,
verbose=1,
use_multiprocessing=True,
workers=14,
callbacks=callbacks )
NameError: name 'y_col' is not defined
#add data_augmentation
train_aug_datagen = ImageDataGenerator(
rescale = 1./255,
rotation_range = 20,
shear_range = 0.1,
zoom_range = 0.2,
width_shift_range = 0.1,
height_shift_range = 0.1,
horizontal_flip = True
)
validation_datagen = ImageDataGenerator(rescale = 1./255)
def two_image_generator(generator,
directory,
batch_size,
shuffle = False,
img_size1 = (224,224),
img_size2 = (299,299)):
gen1 = generator.flow_from_directory(
# This is the target directory
directory,
# All images will be resized to target height and width.
target_size=img_size1,
batch_size=batch_size,
# Since we use categorical_crossentropy loss, we need categorical labels
class_mode='categorical',
shuffle = shuffle,
seed = 7)
gen2 = generator.flow_from_directory(
# This is the target directory
directory,
# All images will be resized to target height and width.
target_size=img_size2,
batch_size=batch_size,
# Since we use categorical_crossentropy loss, we need categorical labels
class_mode='categorical',
shuffle = shuffle,
seed = 7)
while True:
X1i = gen1.next()
X2i = gen2.next()
yield [X1i[0], X2i[0]], X2i[1] #Yield both images and their mutual label
train_generator = two_image_generator(train_aug_datagen,
train_dir,
batch_size = batch_size,
shuffle = True)
validation_generator = two_image_generator(validation_datagen,
validation_dir,
batch_size = batch_size,
shuffle = True)
############################################################################
trained_models_path = './models/VggFace_best_model'
model_names = trained_models_path + '_epoch_{epoch:02d}_val_acc_{val_accuracy:.4f}.hdf5'
checkpoint = ModelCheckpoint(model_names, 'val_accuracy', verbose=1, save_best_only=True)
############################################################################
early = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, mode='auto')
callbacks = [checkpoint,early]
history = model.fit_generator(train_generator,
steps_per_epoch= NUM_TRAIN //batch_size,
epochs=100,
validation_data=validation_generator,
validation_steps= NUM_TEST //batch_size,
verbose=1,
use_multiprocessing=True,
# workers=14,
callbacks=callbacks )
Epoch 1/100
Found 5000 images belonging to 2 classes.
Found 5000 images belonging to 2 classes.
Found 52700 images belonging to 2 classes.
Found 52700 images belonging to 2 classes.
340/625 [===============>..............] - ETA: 4:37 - loss: 7.7634 - acc: 0.4926
import pandas as pd
Related
i want to extract feature by using vgg16 to train ML like (SVM, RF,...) for i can classifie images and visualise them.
i already creat a model by vgg16 but i have diffecult to visualise test images and predict them by using ML.any help please
VGG16 = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet',
include_top=False)
for layer in VGG16.layers:
layer.trainable = False
folders = glob('')
x = Flatten()(VGG16.output)
x = Dense(4096, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(4096, activation='relu')(x)
x = Dropout(0.3)(x)
prediction = Dense(len(folders), activation='sigmoid')(x)
model = Model(inputs=VGG16.input, outputs=prediction)
model.summer()
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('
apple_disease_classification/Train',
target_size = (224, 224),
batch_size = 32,
class_mode = 'categorical')
test_set = test_datagen.flow_from_directory('
apple_disease_classification/Test',
target_size = (224, 224),
batch_size = 32,
class_mode = 'categorical')
r = model.fit_generator(
training_set,
validation_data=test_set,
epochs=50,
steps_per_epoch=len(training_set),
validation_steps=len(test_set))
My results are not reproducible while the global seed is set. Am I doing something wrong or is this an issue of the macbook M1?
I am using:
MacOS 12.5
Python 3.10.6
tensorflow-macos 2.9.2
The terminal is opened using the use rosetta option.
Besides, the model is also unusually bad.
The datasets are from https://www.kaggle.com/competitions/dogs-vs-cats
# Imports.
import tensorflow as tf
import numpy as np
# Some global variables.
seed = 1234
batch_size = 32
learning_rate = 0.001
width = 180
height = 180
epochs = 250
# Set the seed.
tf.random.set_seed(seed);
np.random.seed(seed);
# Create the cnn model
def cnn_model():
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3,3), activation="relu", input_shape=(width, height, 3)),
tf.keras.layers.MaxPooling2D((2,2)),
tf.keras.layers.Conv2D(64, (3,3), activation="relu"),
tf.keras.layers.MaxPooling2D((2,2)),
tf.keras.layers.Conv2D(128, (3,3), activation="relu"),
tf.keras.layers.MaxPooling2D((2,2)),
tf.keras.layers.Conv2D(128, (3,3), activation="relu"),
tf.keras.layers.MaxPooling2D((2,2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(512, activation="relu"),
tf.keras.layers.Dense(1, activation="sigmoid"),
])
opt = tf.keras.optimizers.Adam(learning_rate=0.001)
model.compile(optimizer=opt, loss="binary_crossentropy", metrics=["accuracy"])
return model
# Load an image for predictions.
def load_image(path):
img = tf.keras.preprocessing.image.load_img(
path,
target_size=(width, height),
color_mode="rgb",
)
img_tensor = tf.keras.preprocessing.image.img_to_array(img)
img_tensor = np.expand_dims(img_tensor, axis=0)
img_tensor /= 255.
return img_tensor
# Dump the evaluation metrics to the console.
def dump_metrics(message, model, metrics):
index, s = 0, ""
for name in model.metrics_names:
s += f" - {name}: {round(metrics[index], 3)}"
index += 1
print(f"{message} {s[3:]}")
# Define the image data generator.
train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
rescale = 1./255,
# rotation_range = 45,
# width_shift_range = 0.2,
# height_shift_range = 0.2,
# shear_range = 0.2,
# zoom_range = 0.2
)
val_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)
# Load the training dataset.
train_df = train_datagen.flow_from_directory(
"datasets/train/",
class_mode="binary",
batch_size=batch_size,
target_size=(width, height),
shuffle=True,
color_mode="rgb",
seed=seed,
)
train_samples_count = len(train_df)
# Load the validation dataset.
val_df = val_datagen.flow_from_directory(
"datasets/val/",
class_mode="binary",
batch_size=batch_size,
target_size=(width, height),
shuffle=True,
color_mode="rgb",
seed=seed,
)
val_samples_count = len(train_df)
# Create the model.
model = cnn_model();
model.summary()
# Training callbacks.
callbacks = []
callbacks.append(tf.keras.callbacks.EarlyStopping(monitor="val_loss", patience=5, restore_best_weights=True))
# Train the model.
history = model.fit(
train_df,
steps_per_epoch=train_samples_count // batch_size,
epochs=epochs,
validation_data=val_df,
validation_steps=val_samples_count // batch_size,
callbacks=callbacks,
)
dump_metrics("Training:", model, model.evaluate(train_df, verbose=0))
dump_metrics("Valiation:", model, model.evaluate(val_df, verbose=0))
Failed to find data adapter that can handle input: <class 'tensorflow.python.keras.preprocessing.image.ImageDataGenerator'>, <class 'NoneType'>
I am trying to implement deep learning model and I have run the code more than 10 times , some times in 41, 72,88,100 epoch I got this error, is there anybody to help me
def Tuning_Model():
for k in range(FOLDS):
timestamp = datetime.fromtimestamp(time()).strftime('%Y%m%d-%H%M%S')
output_directory = model_path + "\\" + timestamp
if not os.path.exists(output_directory):
os.makedirs(output_directory)
# Training image augmentation
train_data_generator =tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1. / 255,
fill_mode="constant",
shear_range=0.2,
zoom_range=(0.5, 1),
horizontal_flip=True,
rotation_range=360,
channel_shift_range=25,
brightness_range=(0.75, 1.25))
# Validation image augmentation
val_data_generator =tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1. / 255,
fill_mode="constant",
shear_range=0.2,
zoom_range=(0.5, 1),
horizontal_flip=True,
rotation_range=360,
channel_shift_range=25,
brightness_range=(0.75, 1.25))
test_data_generator =tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255)
train_data_generator = train_data_generator.flow_from_directory(
train_data_path,
target_size = RAW_IMG_SIZE,
batch_size = BATCH_SIZE,
class_mode ='categorical'
)
val_data_generator = val_data_generator.flow_from_directory(
val_data_path,
target_size = RAW_IMG_SIZE,
batch_size = BATCH_SIZE,
class_mode = 'categorical')
test_data = test_data_generator.flow_from_directory(
test_data_path,
target_size = IMG_SIZE,
batch_size = BATCH_SIZE,
class_mode = 'categorical',
shuffle = False)
train_data_generator = crop_generator(train_data_generator, IMG_SIZE)
val_data_generator = crop_generator(val_data_generator, IMG_SIZE)
model = Build_Model(model_name)
model_checkpoint = ModelCheckpoint(output_directory + "\\best_model.hdf5", verbose=1, save_best_only=True)
early_stopping = EarlyStopping(patience=STOPPING_PATIENCE, restore_best_weights=True)
reduce_lr = ReduceLROnPlateau('val_loss', factor=0.5, patience=LR_PATIENCE, min_lr=0.000003125)
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=INITIAL_LR), metrics=['categorical_accuracy'])
history = model.fit_generator(
generator = train_data_generator,
steps_per_epoch = train_image_count // BATCH_SIZE,
epochs = epochs,
validation_data = val_data_generator,
validation_steps= val_image_count // BATCH_SIZE,
callbacks = [model_checkpoint, early_stopping, reduce_lr],
shuffle = False)
# Load the last best model
model = load_model(
output_directory + "\\best_model.hdf5")
# Evaluate model on test set
predictions = model.predict_generator(test_data_generator, test_image_count // BATCH_SIZE + 1)
y_true = test_data_generator.classes
y_pred = np.argmax(predictions, axis=1)
print(classification_report(y_true, y_pred, labels=CLASSES, target_names=CLASS_NAMES))
report = classification_report(y_true, y_pred, labels=CLASSES, target_names=CLASS_NAMES, output_dict=True)
with open(output_directory + '\\classification_report.csv', 'w') as f:
for key in report.keys():
f.write("%s,%s\n" % (key, report[key]))
conf_arr = confusion_matrix(y_true, y_pred, labels=CLASSES)
print(conf_arr)
np.savetxt(output_directory + "\\confusion_matrix.csv", conf_arr, delimiter=",")
# Clear model from GPU after each iteration
print("Finished testing fold {}\n".format(k + 1))
K.clear_session()
k = k + 1
if __name__ == '__main__':
Tuning_Model()
ValueError: Failed to find data adapter that can handle input: <class 'tensorflow.python.keras.preprocessing.image.ImageDataGenerator'>, <class 'NoneType
Have you checked whether training/testing data are in the form of numpy arrays before fitting the model?
Also it might be the case where you are importing model from keras like keras.Sequential() but the generator from Tensorflow like tf.keras.preprocessing.image.ImageDataGenerator().
I'm trying to generate two parameters from ImageDataGenerator for input to my model.fit_generator() but that don't work, I don't now if is the best way to do that.
My structure is:
enter image description here
input_imgen1 = ImageDataGenerator(rescale = 1./255,
vertical_flip=True,
validation_split=0.2,
horizontal_flip = True)
input_imgen2 = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
rotation_range=5.)
testgenerator = ImageDataGenerator(rescale = 1./255)
def generate_generator_multiple(generator1, generator2, train_data_dir, batch_size, img_height,
img_width):
genX1 = generator1.flow_from_directory(train_data_dir,
target_size = (img_height, img_width),
class_mode = 'categorical',
batch_size = batch_size,
shuffle=False,
seed=7)
genX2 = generator2.flow_from_directory(train_data_dir,
target_size = (img_height, img_width),
class_mode = 'categorical',
batch_size = batch_size,
shuffle=False,
seed=7)
while True:
X1i = genX1.next()
X2i = genX2.next()
yield [X1i[0], X2i[0]], X2i[1] #Yield both images and their mutual label
data_gen_train=generate_generator_multiple(generator1=input_imgen1,
generator2=input_imgen2,
train_data_dir=train_dir,
batch_size=batch_size,
img_height=IMG_HEIGHT,
img_width=IMG_WIDTH)
history = model.fit_generator(
data_gen_train,
epochs=epochs,
steps_per_epoch=25,
verbose=1,
validation_data=testgenerator,
validation_steps=25,
callbacks=[checkpoint, early_stop, tensor_board]
)
Error when I fit:
enter image description here
As evident from the logs your error is caused during validation data_gen_valid should be constructed the same way as data_gen_train .
So if your training data has been the concatenation of two generators so should be your validation data.
I am using keras applications for transfer learning with resnet 50 and inception v3 but when predicting always get [[ 0.]]
The below code is for a binary classification problem. I have also tried vgg19 and vgg16 but they work fine, its just resnet and inception. The dataset is a 50/50 split. And I am only changing the model = applications.resnet50.ResNet50 line of code for each model.
below is the code:
from keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
img_width, img_height = 256, 256
train_data_dir = xxx
validation_data_dir = xxx
nb_train_samples = 14000
nb_validation_samples = 6000
batch_size = 16
epochs = 50
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = applications.resnet50.ResNet50(weights = "imagenet", include_top=False, input_shape = (img_width, img_height, 3))
from keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
img_width, img_height = 256, 256
train_data_dir = xxx
validation_data_dir = xxx
nb_train_samples = 14000
nb_validation_samples = 6000
batch_size = 16
epochs = 50
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = applications.resnet50.ResNet50(weights = "imagenet", include_top=False, input_shape = (img_width, img_height, 3))
#Freeze the layers which you don't want to train. Here I am freezing the first 5 layers.
for layer in model.layers[:5]:
layer.trainable = False
#Adding custom Layers
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
x = Dropout(0.5)(x)
#x = Dense(1024, activation="relu")(x)
predictions = Dense(1, activation="sigmoid")(x)
# creating the final model
model_final = Model(input = model.input, output = predictions)
# compile the model
model_final.compile(loss = "binary_crossentropy", optimizer = optimizers.SGD(lr=0.0001, momentum=0.9), metrics=["accuracy"])
# Initiate the train and test generators with data Augumentation
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
# Save the model according to the conditions
#checkpoint = ModelCheckpoint("vgg16_1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
#early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')
model_final.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size,
callbacks=[early_stopping])
from keras.models import load_model
import numpy as np
from keras.preprocessing.image import img_to_array, load_img
#test_model = load_model('vgg16_1.h5')
img = load_img('testn7.jpg',False,target_size=(img_width,img_height))
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
#preds = model_final.predict_classes(x)
prob = model_final.predict(x, verbose=0)
#print(preds)
print(prob)
Note That model_final.evaluate_generator(validation_generator, nb_validation_samples) provides an expected accuracy like 80% its just predict that is always 0.
Just find it strange that vgg19 and vgg16 work fine but not resnet50 and inception. Do these models require something else to work?
Any insight would be great.
Thanks in advance.
I was running into similar problem. You are scaling all the RGB values from 0-255 to 0-1 during training.
Thse same should be done at the time of prediction.
Try
x = img_to_array(img)
x = x/255