Fix regularizers not imported

chatbot_constructor.py

@@ -3,6 +3,7 @@ import numpy as np
 from keras.models import Model
 from keras.saving import load_model
 from keras.layers import *
+from keras.regularizers import L1
 from tensorflow.keras.optimizers import RMSprop
 from keras.preprocessing.text import Tokenizer
 import os
@@ -57,9 +58,9 @@ def train(message: str = "", regularization: float = 0.0001, dropout: float = 0.
         dropout1_layer = Dropout(dropout)(emb_layer)
         attn_layer = MultiHeadAttention(num_heads=4, key_dim=128)(dropout1_layer, dropout1_layer, dropout1_layer)
         noise_layer = GaussianNoise(0.1)(attn_layer)
-        conv1_layer = Conv1D(kernels_count, kernel_size, padding='same', activation='relu', strides=1, input_shape=(64, 128), kernel_regularizer=regularizers.L1(regularization))(noise_layer)
-        conv2_layer = Conv1D(16, 4, padding='same', activation='relu', strides=1, kernel_regularizer=regularizers.L1(regularization))(conv1_layer)
-        conv3_layer = Conv1D(8, 2, padding='same', activation='relu', strides=1, kernel_regularizer=regularizers.L1(regularization))(conv2_layer)
+        conv1_layer = Conv1D(kernels_count, kernel_size, padding='same', activation='relu', strides=1, input_shape=(64, 128), kernel_regularizer=L1(regularization))(noise_layer)
+        conv2_layer = Conv1D(16, 4, padding='same', activation='relu', strides=1, kernel_regularizer=L1(regularization))(conv1_layer)
+        conv3_layer = Conv1D(8, 2, padding='same', activation='relu', strides=1, kernel_regularizer=L1(regularization))(conv2_layer)
         flatten_layer = Flatten()(conv3_layer)
         attn_flatten_layer = Flatten()(attn_layer)
         conv1_flatten_layer = Flatten()(conv1_layer)
@@ -67,16 +68,16 @@ def train(message: str = "", regularization: float = 0.0001, dropout: float = 0.
         conv3_flatten_layer = Flatten()(conv3_layer)
         concat1_layer = Concatenate()([flatten_layer, attn_flatten_layer, conv1_flatten_layer, conv2_flatten_layer, conv3_flatten_layer])
         dropout2_layer = Dropout(dropout)(concat1_layer)
-        dense1_layer = Dense(512, activation="linear", kernel_regularizer=regularizers.L1(regularization))(dropout2_layer)
+        dense1_layer = Dense(512, activation="linear", kernel_regularizer=L1(regularization))(dropout2_layer)
         prelu1_layer = PReLU()(dense1_layer)
         dropout3_layer = Dropout(dropout)(prelu1_layer)
-        dense2_layer = Dense(256, activation="tanh", kernel_regularizer=regularizers.L1(regularization))(dropout3_layer)
+        dense2_layer = Dense(256, activation="tanh", kernel_regularizer=L1(regularization))(dropout3_layer)
         dropout4_layer = Dropout(dropout)(dense2_layer)
-        dense3_layer = Dense(256, activation="relu", kernel_regularizer=regularizers.L1(regularization))(dropout4_layer)
+        dense3_layer = Dense(256, activation="relu", kernel_regularizer=L1(regularization))(dropout4_layer)
         dropout5_layer = Dropout(dropout)(dense3_layer)
-        dense4_layer = Dense(100, activation="tanh", kernel_regularizer=regularizers.L1(regularization))(dropout5_layer)
+        dense4_layer = Dense(100, activation="tanh", kernel_regularizer=L1(regularization))(dropout5_layer)
         concat2_layer = Concatenate()([dense4_layer, prelu1_layer, attn_flatten_layer, conv1_flatten_layer])
-        dense4_layer = Dense(resps_len, activation="softmax", kernel_regularizer=regularizers.L1(regularization))(concat2_layer)
+        dense4_layer = Dense(resps_len, activation="softmax", kernel_regularizer=L1(regularization))(concat2_layer)
         model = Model(inputs=input_layer, outputs=dense4_layer)
         
         X = []