Not able to use the model.fit() in Keras - tensorflow

Question

Please help me run the code. This doesn't work. I have been trying hard to solve this for days. The model_final.fit() also doesn't work. Why is this showing the same error for the method. This is almost the entire code.
EDIT: The train_generator and validation_generator added.
img_width, img_height = 400, 400
train_data_dir = "data/train"
validation_data_dir = "data/validation"
nb_train_samples = 4125
nb_validation_samples = 466
batch_size = 16,
epochs = 5
output_num_classes = 2
K.set_image_data_format('channels_last')
model = applications.VGG19(weights="imagenet", include_top=False, input_shape=(img_width, img_height, 3))
model.summary()
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(output_num_classes, activation="softmax")(x)
model_final = Model(inputs=model.input, outputs=predictions)
model_final.compile(loss="categorical_crossentropy",
optimizer=optimizers.SGD(learning_rate=0.0001, momentum=0.9),
metrics=['accuracy'])
train_datagen = ImageDataGenerator(rescale=1./255,
zoom_range=0.3,
width_shift_range=0.3,
height_shift_range=0.3,
rotation_range=30,
horizontal_flip=True,
fill_mode='nearest')
train_generator = train_datagen.flow_from_directory(train_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical')
validation_datagen = ImageDataGenerator(rescale=1./255,
zoom_range=0.3,
width_shift_range=0.3,
height_shift_range=0.3,
rotation_range=30,
horizontal_flip=True,
fill_mode='nearest')
validation_generator = validation_datagen.flow_from_directory(validation_data_dir,
target_size=(img_height, img_width),
batch_size = batch_size,
class_mode='categorical')
The code above is before fitting.
model_final.fit_generator(train_generator,
steps_per_epoch=nb_train_samples,
epochs = epochs,
validation_data = validation_generator,
validation_steps = nb_validation_samples)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
warnings.warn('`Model.fit_generator` is deprecated and '
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-87-b195f52ce8fb> in <module>()
3 epochs = epochs,
4 validation_data = validation_generator,
----> 5 validation_steps = nb_validation_samples)
/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/iterator.py in __len__(self)
66
67 def __len__(self):
---> 68 return (self.n + self.batch_size - 1) // self.batch_size # round up
69
70 def on_epoch_end(self):
TypeError: unsupported operand type(s) for +: 'int' and 'tuple' ```

Answer 1

I was able to replicate your issue with sample code as shown below
import tensorflow as tf
from tensorflow.keras.applications import ResNet50
import os
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Sequential
from google.colab import drive
drive.mount('/content/drive')
train_dir = '/content/drive/My Drive/Dogs_Vs_Cats/train'
test_dir = '/content/drive/My Drive/Dogs_Vs_Cats/test'
img_width, img_height = 224, 224
input_shape = (img_width, img_height, 3)
epochs = 8
batch_size = 32,
train_datagen = ImageDataGenerator(
rescale = 1. /255,
horizontal_flip = True)
test_datagen = ImageDataGenerator(
rescale = 1. /255)
train_data = train_datagen.flow_from_directory(
train_dir,
target_size = (img_width, img_height),
batch_size = batch_size,
class_mode = 'binary')
test_data = test_datagen.flow_from_directory(
test_dir,
target_size = (img_width, img_height),
class_mode = 'binary')
Conv_Base = ResNet50(include_top = False, weights = 'imagenet', input_shape = input_shape)
model = Sequential()
model.add(Conv_Base)
model.add(Flatten())
model.add(Dense(units = 256, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(units = 1, activation = 'sigmoid'))
model.summary()
model.compile(loss = 'binary_crossentropy',
optimizer = 'Adam',
metrics = [tf.keras.metrics.Precision(), tf.keras.metrics.Recall()])
model.fit(
train_data,
steps_per_epoch = 10,
epochs = 2,
validation_data = test_data,
verbose = 1,
validation_steps = 32)
Output:
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
Found 2000 images belonging to 2 classes.
Found 1018 images belonging to 2 classes.
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
resnet50 (Functional) (None, 7, 7, 2048) 23587712
_________________________________________________________________
flatten (Flatten) (None, 100352) 0
_________________________________________________________________
dense (Dense) (None, 256) 25690368
_________________________________________________________________
dropout (Dropout) (None, 256) 0
_________________________________________________________________
dense_1 (Dense) (None, 1) 257
=================================================================
Total params: 49,278,337
Trainable params: 49,225,217
Non-trainable params: 53,120
_________________________________________________________________
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-5a5644117355> in <module>()
60 validation_data = test_data,
61 verbose = 1,
---> 62 validation_steps = 32)
3 frames
/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/iterator.py in __len__(self)
66
67 def __len__(self):
---> 68 return (self.n + self.batch_size - 1) // self.batch_size # round up
69
70 def on_epoch_end(self):
TypeError: unsupported operand type(s) for +: 'int' and 'tuple'
Solution to fix:
As rightly suggested by #Dr. Snoopy, this issue was due to comma right after batch_size definition. It converts the integer into a tuple and which is not supported.
Changing batch_size = 32, to batch_size = 32 has resolved the issue.
Working code as shown below
import tensorflow as tf
from tensorflow.keras.applications import ResNet50
import os
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Sequential
from google.colab import drive
drive.mount('/content/drive')
train_dir = '/content/drive/My Drive/Dogs_Vs_Cats/train'
test_dir = '/content/drive/My Drive/Dogs_Vs_Cats/test'
img_width, img_height = 224, 224
input_shape = (img_width, img_height, 3)
epochs = 8
batch_size = 32 # removed comma
train_datagen = ImageDataGenerator(
rescale = 1. /255,
horizontal_flip = True)
test_datagen = ImageDataGenerator(
rescale = 1. /255)
train_data = train_datagen.flow_from_directory(
train_dir,
target_size = (img_width, img_height),
batch_size = batch_size,
class_mode = 'binary')
test_data = test_datagen.flow_from_directory(
test_dir,
target_size = (img_width, img_height),
class_mode = 'binary')
Conv_Base = ResNet50(include_top = False, weights = 'imagenet', input_shape = input_shape)
model = Sequential()
model.add(Conv_Base)
model.add(Flatten())
model.add(Dense(units = 256, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(units = 1, activation = 'sigmoid'))
model.summary()
model.compile(loss = 'binary_crossentropy',
optimizer = 'Adam',
metrics = [tf.keras.metrics.Precision(), tf.keras.metrics.Recall()])
model.fit(
train_data,
steps_per_epoch = 10,
epochs = 2,
validation_data = test_data,
verbose = 1,
validation_steps = 32)
Output:
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
Found 2000 images belonging to 2 classes.
Found 1018 images belonging to 2 classes.
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
resnet50 (Functional) (None, 7, 7, 2048) 23587712
_________________________________________________________________
flatten_1 (Flatten) (None, 100352) 0
_________________________________________________________________
dense_2 (Dense) (None, 256) 25690368
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_3 (Dense) (None, 1) 257
=================================================================
Total params: 49,278,337
Trainable params: 49,225,217
Non-trainable params: 53,120
_________________________________________________________________
Epoch 1/2
10/10 [==============================] - 107s 10s/step - loss: 9.5302 - precision_1: 0.6096 - recall_1: 0.6485 - val_loss: 14405.0479 - val_precision_1: 0.0000e+00 - val_recall_1: 0.0000e+00
Epoch 2/2
10/10 [==============================] - 68s 7s/step - loss: 2.2295 - precision_1: 0.8191 - recall_1: 0.8799 - val_loss: 3977.1418 - val_precision_1: 0.4912 - val_recall_1: 1.0000
<tensorflow.python.keras.callbacks.History at 0x7fbba005cc50>