Сеть многому не учится

Question

Я обучаю очень простую сеть на своем наборе данных RGB-изображений, однако, похоже, что сеть не многому учится, точность val не улучшается с самого начала, точность обучения улучшается, но очень мало. Что я делаю неправильно? Это простая сеть, поэтому я не уверен, что так ужасно неправильно.

import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGenerator
import os
from keras import layers
from keras import models
from keras import optimizers
from keras.layers import Dropout
from keras.preprocessing.image import img_to_array, load_img

os.environ["CUDA_VISIBLE_DEVICES"]="0"

train_dir = '/home/d/Desktop/Bl/data/train'
eval_dir = '/home/d/Desktop/Bl/data/eval'
test_dir = '/home/d/Desktop/Bl/data/test'


# create a data generator
train_datagen = ImageDataGenerator(rescale=1./255,   #Scale the image between 0 and 1
                                    rotation_range=40,
                                    width_shift_range=0.2,
                                    height_shift_range=0.2,
                                    shear_range=0.2,
                                    zoom_range=0.2,
                                    horizontal_flip=True,)

val_datagen = ImageDataGenerator(rescale=1./255)  #We do not augment validation data. we only perform rescale

test_datagen = ImageDataGenerator(rescale=1./255)  #We do not augment validation data. we only perform rescale

# load and iterate training dataset
train_generator = train_datagen.flow_from_directory(train_dir, class_mode='categorical', batch_size=16, shuffle='True', seed=42)
# load and iterate validation dataset
val_generator = val_datagen.flow_from_directory(eval_dir, class_mode='categorical', batch_size=16, shuffle='True', seed=42)
# load and iterate test dataset
test_generator = test_datagen.flow_from_directory(test_dir, class_mode=None, batch_size=1, shuffle='False', seed=42)




model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(256, 256, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(Dropout(0.2))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))  #Dropout for regularization
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(3, activation='softmax'))  #Sigmoid function at the end because we have just two classes

#Lets see our model
model.summary()

model.compile(loss='categorical_crossentropy', optimizer=optimizers.SGD(lr=1e-7, momentum=0.9), metrics=['acc']) 
#Adam(lr=0.000001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0), metrics=['acc']) 

#The training part
history = model.fit_generator(train_generator,
                              steps_per_epoch=train_generator.n // train_generator.batch_size,
                              epochs=200,
                              validation_data=val_generator,
                              validation_steps=val_generator.n // val_generator.batch_size)

#Save the model
model.save_weights('/home/d/Desktop/Bl/model_weights.h5')
model.save('/home/d/Desktop/Bl/model_keras.h5')

#lets plot the train and val curve
#get the details form the history object
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)

#Train and validation accuracy
plt.plot(epochs, acc, 'b', label='Training accuracy')
plt.plot(epochs, val_acc, 'r', label='Validation accuracy')
plt.title('Training and Validation accurarcy')
plt.legend()

plt.figure()
#Train and validation loss
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title('Training and Validation loss')
plt.legend()

plt.show()