keras низкая точность нейросети при обучение EMNIST

Столкнулся с такой вот проблемой, обучаю свою нейросеть при помощи базы данных EMNIST и итоговая точность составляет не более 0.59. При всем этом обучение занимает целых 5 часов, обучение происходит за 30 эпох, точность с начала обучения составляет около 0.015, постепенно растет, но где-то после 10й эпохи начинает расти очень медленно. В чем может быть причина такой низкой точности и как это можно исправить?

Начало обучения:

Код:

from tensorflow import keras
from keras.models import Sequential
from keras import optimizers
from keras.layers import Convolution2D, MaxPooling2D, Dropout, Flatten, Dense, Reshape, LSTM, BatchNormalization
from keras.optimizers import SGD, RMSprop, Adam
from keras import backend as K
from keras.constraints import maxnorm
import tensorflow as tf
import tensorflow.python.keras.backend as K

tf.compat.v1.disable_eager_execution()

emnist_labels = [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]
model = Sequential()
model.add(Convolution2D(filters=32, kernel_size=(3, 3), padding='valid', input_shape=(28, 28, 1), activation='relu'))
model.add(Convolution2D(filters=64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(emnist_labels), activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])

emnist_path = 'D:\\Temp\\1\\'
X_train = idx2numpy.convert_from_file(emnist_path + 'emnist-byclass-train-images-idx3-ubyte')
y_train = idx2numpy.convert_from_file(emnist_path + 'emnist-byclass-train-labels-idx1-ubyte')

X_test = idx2numpy.convert_from_file(emnist_path + 'emnist-byclass-test-images-idx3-ubyte')
y_test = idx2numpy.convert_from_file(emnist_path + 'emnist-byclass-test-labels-idx1-ubyte')

X_train = np.reshape(X_train, (X_train.shape[0], 28, 28, 1))
X_test = np.reshape(X_test, (X_test.shape[0], 28, 28, 1))

print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, len(emnist_labels))

k = 10
X_train = X_train[:X_train.shape[0] // k]
y_train = y_train[:y_train.shape[0] // k]
X_test = X_test[:X_test.shape[0] // k]
y_test = y_test[:y_test.shape[0] // k]


X_train = X_train.astype(np.float32)
X_train /= 255.0
X_test = X_test.astype(np.float32)
X_test /= 255.0

x_train_cat = keras.utils.to_categorical(y_train, len(emnist_labels))
y_test_cat = keras.utils.to_categorical(y_test, len(emnist_labels))


learning_rate_reduction = keras.callbacks.ReduceLROnPlateau(monitor='val_accuracy', patience=3,   verbose=1, factor=0.5, min_lr=0.00001)


K.get_session().run(tf.compat.v1.global_variables_initializer())
model.fit(X_train, x_train_cat, validation_data=(X_test, y_test_cat), callbacks=[learning_rate_reduction], batch_size=64, epochs=30)

model.save('emnist_letters1.h5')