AttributeError: 'str' object has no attribute 'flow_from_directory' - python-3.x

Question

so I tried to make a classification report for my machine learning project it is an indigenous language translator using individual symbols as data sets (python 3.6) it runs it can show me the accuracy, confusion matrix but it's not showing me my F1 score and other of my classification report
import numpy
import numpy as np
from sklearn.linear_model import LogisticRegression
from tensorflow import keras, metrics
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Model
from sklearn.metrics import confusion_matrix
import itertools
import matplotlib.pyplot as plt
from webencodings import labels
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
train_path=r'C:\Users\Acer\imagerec\BAYBAYIN\TRAIN'
valid_path=r'C:\Users\Acer\imagerec\BAYBAYIN\VAL'
test_path=r'C:\Users\Acer\imagerec\BAYBAYIN\TEST'
batch_size=30
class_labels=['A', 'BA', 'KA', 'GA', 'HA', '1', '2', '3', '4', '5', '6', '7',
'8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19',
'20', '21', '22', '23', '24', '25', '26', '28', '29', '30', '31', '32',
'33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44']
train_batches=ImageDataGenerator(preprocessing_function=keras.applications.xception.preprocess_input)\
.flow_from_directory(train_path, target_size=(299,299),classes=class_labels,batch_size=5)
valid_batches=ImageDataGenerator(preprocessing_function=keras.applications.xception.preprocess_input)\
.flow_from_directory(valid_path, target_size=(299,299),classes=class_labels,batch_size=5)
test_batches=ImageDataGenerator(preprocessing_function=keras.applications.xception.preprocess_input)\
.flow_from_directory(test_path, target_size=(299,299),classes=class_labels,batch_size=5, shuffle=False)
base_model=keras.applications.vgg19.VGG19(include_top=False)
x=base_model.output
x=GlobalAveragePooling2D()(x)
x=Dense(1024, activation='relu')(x)
x=Dense(48, activation='softmax')(x)
model=Model(inputs=base_model.input, outputs=x)
base_model.trainable = False
N=1
print("HANG ON LEARNING IN PROGRESS...")
model.compile(Adam(lr=.0001),loss='categorical_crossentropy', metrics=['accuracy'])
history=model.fit_generator(train_batches, steps_per_epoch=1290, validation_data=valid_batches,
validation_steps=90,epochs=N,verbose=1)
print("[INFO]evaluating model...")
test_labels=test_batches.classes
predictions=model.predict_generator(test_batches, steps=28, verbose=1)
import matplotlib.pyplot as plt
import numpy as np
plt.imshow(np.random.random((48,48)), interpolation='nearest')
plt.xticks(np.arange(0,48), ['A', 'BA', 'KA', 'GA', 'HA', '1', '2', '3', '4', '5', '6', '7',
'8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19',
'20', '21', '22', '23', '24', '25', '26', '28', '29', '30', '31', '32',
'33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44'])
plt.yticks(np.arange(0,48),['A', 'BA', 'KA', 'GA', 'HA', '1', '2', '3', '4', '5', '6', '7',
'8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19',
'20', '21', '22', '23', '24', '25', '26', '28', '29', '30', '31', '32',
'33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44'])
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_path.flow_from_directory(
train_path,
batch_size=batch_size,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
valid_path,
batch_size=batch_size,
class_mode='categorical')
model.fit_generator(
train_generator,
steps_per_epoch= 52800 // batch_size,
epochs=N,
validation_data=validation_generator,
validation_steps= 13200 // batch_size)
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import seaborn as sns
test_steps_per_epoch = numpy.math.ceil(validation_generator.samples / validation_generator.batch_size)
predictions = model.predict_generator(validation_generator, steps=test_steps_per_epoch)
test_steps_per_epoch = numpy.math.ceil(validation_generator.samples / validation_generator.batch_size)
predicted_classes = numpy.argmax(predictions, axis=1)
true_classes = validation_generator.classes
class_labels = list(validation_generator.class_indices.keys())
report = classification_report(true_classes, predicted_classes, target_names=class_labels)
print(report)
cm=confusion_matrix(true_classes,predicted_classes)
print(cm)
plt.show()
model.save("X19baybayin.h5")
I get these errors I dont know why
Traceback (most recent call last):
File "C:/Users/Acer/PycharmProjects/translator/mine.py", line 80, in <module>
train_generator = train_path.flow_from_directory(
AttributeError: 'str' object has no attribute 'flow_from_directory'

Answer 1

You assigned a variable train_path as string:
train_path=r'C:\Users\Acer\imagerec\BAYBAYIN\TRAIN'
And then on line 80 you are trying to call method on string:
train_generator = train_path.flow_from_directory(
train_path,
batch_size=batch_size,
class_mode='categorical')
Perhaps you wanted to call it on train_batches instead?
Here are the docs with this method usage examples.