Tensorflow model validation accuracy not increasing

Question

I have built a tensorflow model and am getting no change in my validation accuracy in different epochs, which makes me believe there is something wrong in my setup. Below is my code.

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import regularizers
import tensorflow as tf

model = Sequential()
model.add(Conv2D(16, (3, 3), input_shape=(299, 299,3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))

model.add(Conv2D(32, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))

model.add(Conv2D(64, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))

model.add(Conv2D(64, (3, 3),padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))

# this converts our 3D feature maps to 1D feature vectors
model.add(Flatten())  

model.add(Dense(512))
model.add(Activation('relu'))

model.add(Dropout(0.5))

model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

batch_size=32

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
        rescale=1./255,
        # shear_range=0.2,
        # zoom_range=0.2,
        horizontal_flip=True)

# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1./255)

# this is a generator that will read pictures found in
# subfolers of 'data/train', and indefinitely generate
# batches of augmented image data
train_generator = train_datagen.flow_from_directory(
        'Documents/Training',  # this is the target directory
         target_size=(299, 299),  #all images will be resized to 299
        batch_size=batch_size,
        class_mode='binary')  # since we use binary_crossentropy loss, we need binary labels

# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
        'Documents/Dev',
        target_size=(299, 299),
        batch_size=batch_size,
        class_mode='binary')
#w1 =  tf.Variable(tf.truncated_normal([784, 30], stddev=0.1))
model.fit_generator(
        train_generator,
        steps_per_epoch=50 // batch_size,
        verbose = 1,
        epochs=10,
        validation_data=validation_generator,
        validation_steps=8 // batch_size)

Which when I run produces the following output. Anything I'm missing here as far as my architecture is concerned or data generation steps? I have referenced Tensorflow model accuracy not increasing and accuracy not increasing in tensorflow model to no avail yet.

Epoch 1/10
3/3 [==============================] - 2s 593ms/step - loss: 0.6719 - accuracy: 0.6250 - val_loss: 0.8198 - val_accuracy: 0.5000
Epoch 2/10
3/3 [==============================] - 2s 607ms/step - loss: 0.6521 - accuracy: 0.6667 - val_loss: 0.8518 - val_accuracy: 0.5000
Epoch 3/10
3/3 [==============================] - 2s 609ms/step - loss: 0.6752 - accuracy: 0.6250 - val_loss: 0.7129 - val_accuracy: 0.5000
Epoch 4/10
3/3 [==============================] - 2s 611ms/step - loss: 0.6841 - accuracy: 0.6250 - val_loss: 0.7010 - val_accuracy: 0.5000
Epoch 5/10
3/3 [==============================] - 2s 608ms/step - loss: 0.6977 - accuracy: 0.5417 - val_loss: 0.6551 - val_accuracy: 0.5000
Epoch 6/10
3/3 [==============================] - 2s 607ms/step - loss: 0.6508 - accuracy: 0.7083 - val_loss: 0.5752 - val_accuracy: 0.5000
Epoch 7/10
3/3 [==============================] - 2s 615ms/step - loss: 0.6596 - accuracy: 0.6875 - val_loss: 0.9326 - val_accuracy: 0.5000
Epoch 8/10
3/3 [==============================] - 2s 604ms/step - loss: 0.7022 - accuracy: 0.6458 - val_loss: 0.6976 - val_accuracy: 0.5000
Epoch 9/10
3/3 [==============================] - 2s 591ms/step - loss: 0.6331 - accuracy: 0.7292 - val_loss: 0.9571 - val_accuracy: 0.5000
Epoch 10/10
3/3 [==============================] - 2s 595ms/step - loss: 0.6085 - accuracy: 0.7292 - val_loss: 0.6029 - val_accuracy: 0.5000
Out[24]: <keras.callbacks.callbacks.History at 0x1ee4e3a8f08>

Answer 1

You are setting the training steps per epoch =50//32=1. So do you only have 50 training images? Similarly for validation you have steps = 8//32=0. Do you have only 8 validation images? When you execute the program how many images do the training and validation generators print out they have found? You will need more images than that. Try setting your batch size =1