Attention layer

[Tailor2019]

@andbue
Hello
Can you please help me to replace the ctc part in this ocr with an attention layer ?
I'm trying to use this code
`def get_2d_conv_LSTM_atten_model(n):
''' Create a standard deep 2D convolutional neural network'''
nclass = 8
inp = Input(shape=(n,216,1)) #2D matrix of 30 MFCC bands by 216 audio length.
x = Convolution2D(64, (3,3), strides=(1, 1), padding="same")(inp) #(4,10)
x = BatchNormalization()(x)
x = Activation("relu")(x)
x = MaxPool2D()(x)
x = Dropout(rate=0.2)(x)
x = Convolution2D(128, (3,3), strides=(1, 1), padding="same")(x)
x = BatchNormalization()(x)
x = Activation("relu")(x)
x = MaxPool2D()(x)
x = Dropout(rate=0.2)(x)

x = Convolution2D(256, (3,3), strides=(1, 1), padding="same")(x)
x = BatchNormalization()(x)
x = Activation("relu")(x)
x = MaxPool2D()(x)
x = Dropout(rate=0.2)(x)

x = Convolution2D(128, (3,3), strides=(1, 1), padding="same")(x)
x = BatchNormalization()(x)
x = Activation("relu")(x)
x = MaxPool2D()(x)
x = Dropout(rate=0.2)(x)

x = Reshape((-1, 128))(x)
#LSTM
x = LSTM(32, return_sequences=True)(x)
x = SeqSelfAttention(attention_activation ='tanh')(x)
x = LSTM(32, return_sequences=False)(x)

out = Dense(nclass, activation=softmax)(x)
model = models.Model(inputs=inp, outputs=out)

opt = tf.keras.optimizers.Adam(learning_rate = 0.0001, decay=1e-6)
model.compile(optimizer=opt, loss=losses.categorical_crossentropy, metrics=['acc'])
return model`

Normalization as per the standard NN process

X_train_norm, X_test_norm, X_val_norm = data_normalization(X_train, X_test, X_val)

one hot encode the target

lb = LabelEncoder()
y_train_cat = np_utils.to_categorical(lb.fit_transform(y_train)) # for GPU
y_test_cat = np_utils.to_categorical(lb.fit_transform(y_test)) # for GPU
y_val_cat = np_utils.to_categorical(lb.fit_transform(y_val)) # for GPU
#y_train = to_categorical(lb.fit_transform(y_train)) # for TPU
#y_test = to_categorical(lb.fit_transform(y_test)) # for TPU
#y_val = to_categorical(lb.fit_transform(y_val)) # for TPU

print(X_train.shape)
print(lb.classes_)

model = get_2d_conv_LSTM_atten_model(n_mfcc)
model.summary()
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=15)
model_name = 'best_model.h5'
save_dir = os.path.join(os.getcwd(), 'saved_models')
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
print('Save model and weights at %s ' % model_path)
mc = ModelCheckpoint(model_path, monitor='val_acc', mode='max', verbose=1,
save_best_only=True)
model_history = model.fit(X_train_norm, y_train_cat, validation_data=(X_val_norm, y_val_cat), batch_size=32,
verbose = 1, epochs=200, callbacks=[es, mc])

results = get_results(model_history,model,X_test_norm,y_test_cat, ref.label.unique())
results.create_plot(model_history)

model_json = model.to_json()
with open("model_json.json", "w") as json_file:
json_file.write(model_json)

loading json and model architecture

json_file = open('model_json.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
from keras.models import model_from_json
loaded_model = model_from_json(loaded_model_json, custom_objects={'SeqSelfAttention': SeqSelfAttention})

load weights into new model

loaded_model.load_weights(model_path)#("saved_models/best_model.h5")
print("Loaded model from disk")
But I failed to turn this model
Please help how can I use attention model with calamari
Thank you in advance