Error Encountered: InvalidArgumentError: Graph execution error using Keras and Transformers

Question

I'm facing difficulties in training multiple models using Keras and Transformers. Below are the code snippets of the models and the errors encountered during training:

• Model 1: LSTM Model

(X_train, y_train), (X_test, y_test) = keras.datasets.imdb.load_data()
X_train = X_train[:2500]
y_train = y_train[:2500]
X_test = X_test[:500]
y_test = y_test[:500]

def dekodeeri(tekstijada):
    # Abifunktsioon numbritest tagasi tähtede saamiseks
    word_index = keras.datasets.imdb.get_word_index()
    index_word = {0: "<PAD>", 1: "<START>", 2: "<UNK>", 3: "<UNUSED>"}
    index_word[1] = "[START]"
    index_word[2] = "[OOV]"
    for (word, i) in word_index.items():
        index_word[i + 3] = word
    return " ".join(index_word[i] for i in tekstijada)

print(X_train.shape,y_train.shape)
print(X_test.shape,y_test.shape)
print()
print(X_train[0])
print(dekodeeri(X_train[0]))
print(y_train[0])

max_features = 100000
maxlen = 500

# Padding sequences
print('Pad sequences (samples x time)')
X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)

model = Sequential()
model.add(Embedding(max_features, 256))
model.add(SpatialDropout1D(0.4))
model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train the model
model.fit(X_train, y_train, batch_size=64, epochs=5, validation_data=(X_test, y_test))

• Model 2

import tensorflow as tf
from transformers import TFDistilBertModel, DistilBertConfig
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, GlobalAveragePooling1D

# Define input shape
input_layer = Input(shape=(maxlen,), dtype=tf.int32)

# Load DistilBERT model
config = DistilBertConfig(dropout=0.2, attention_dropout=0.2)
distil_bert_model = TFDistilBertModel.from_pretrained('distilbert-base-uncased', config=config)

# Freeze DistilBERT layers
for layer in distil_bert_model.layers:
    layer.trainable = False

# Get DistilBERT output
distil_bert_output = distil_bert_model(input_layer)[0]

# Add pooling layer
pooled_output = GlobalAveragePooling1D()(distil_bert_output)

# Add dense layer for classification
output_layer = Dense(1, activation='sigmoid')(pooled_output)

# Create model
model_2_1 = Model(inputs=input_layer, outputs=output_layer)

# Compile the model
model_2_1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train the model
model_2_1.fit(X_train_padded, y_train, batch_size=64, epochs=5, validation_data=(X_test_padded, y_test))

Error Encountered:

InvalidArgumentError: Graph execution error. InvalidArgumentError Traceback (most recent call last) in <cell line: 36>() 34 model_2_1.summary() 35 # Hindamistulemuste saamine ---> 36 model_2_1.fit(X_train_padded, y_train, batch_size=64, epochs=5, validation_data=(X_test_padded, y_test)) 1 frames /usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 52 try:

Similar issues were encountered with Model 3 and Model 4 as well.

I've ensured that the input data shapes, data types, and preprocessing steps are correct