Train accuracy improving but validation remains unchanged? - tensorflow

Question

I am using TF 2.0. I was trying to train a network on my own data. It was not going well. The validation accuracy was close to 0 and stagnant. I tried many regularizations to no effect. Then I tried training a network on 3 classes of data where all images in each class are the same so as to eliminate the possibility of variability. But this is not working either. Since all in-class images are the same, I would expect the validation accuracy to perfectly match the training accuracy since there is no new data. Why is that not the case? Here is my code:
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.applications.mobilenet import preprocess_input
import matplotlib.pyplot as plt
base_model = tf.keras.applications.MobileNet(weights='imagenet', include_top=False)
def turn_off(n):
for layer in model.layers[:n]:
layer.trainable = False
for layer in model.layers[n:]:
layer.trainable = True
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(
x) # we add dense layers so that the model can learn more complex functions and classify for better results.
x = Dense(1024, activation='relu')(x) # dense layer 2
x = Dense(512, activation='relu')(x) # dense layer 3
preds = Dense(3, activation='softmax')(x) # final layer with softmax activation
model = Model(inputs=base_model.input, outputs=preds)
turn_off(87)
train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input,
rescale=1. / 255,
validation_split=0.2) # set validation split
train_generator = train_datagen.flow_from_directory(
'/users/josh.flori/desktop/colors/',
target_size=(224, 224),
batch_size=32,
color_mode='rgb',
class_mode='categorical',
subset='training',
shuffle=True) # set as training data
validation_generator = train_datagen.flow_from_directory(
'/users/josh.flori/desktop/colors/',
target_size=(224, 224),
batch_size=32,
color_mode='rgb',
class_mode='categorical',
subset='validation',
shuffle=True) # set as validation data
# Adam optimizer
# loss function will be categorical cross entropy
# evaluation metric will be accuracy
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit_generator(
train_generator,
steps_per_epoch=train_generator.samples // train_generator.batch_size,
validation_data=validation_generator,
validation_steps=validation_generator.samples // train_generator.batch_size,
epochs=6)
Here is the training output
9/9 [==============================] - 19s 2s/step - loss: 0.2645 - accuracy: 0.9134 - val_loss: 1.6668 - val_accuracy: 0.3438
Epoch 2/6
9/9 [==============================] - 20s 2s/step - loss: 0.0417 - accuracy: 0.9567 - val_loss: 2.6176 - val_accuracy: 0.3438
Epoch 3/6
9/9 [==============================] - 17s 2s/step - loss: 0.4771 - accuracy: 0.9422 - val_loss: 4.0694 - val_accuracy: 0.3438
Epoch 4/6
9/9 [==============================] - 18s 2s/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 2.1304 - val_accuracy: 0.3125
Epoch 5/6
9/9 [==============================] - 18s 2s/step - loss: 9.7658e-07 - accuracy: 1.0000 - val_loss: 3.1633 - val_accuracy: 0.3125
Epoch 6/6
9/9 [==============================] - 18s 2s/step - loss: 2.2571e-05 - accuracy: 1.0000 - val_loss: 3.4949 - val_accuracy: 0.3125
My image folders look like this
where there are exactly 128 identical images per folder.
I've been reading all day, trying different images, I can't seem to get anywhere. What is causing this particular behavior? It has to be something obvious but I'm not sure.