UnimplementedError:Cast string to float is not supported

Question

UnimplementedError Traceback (most recent call last)

UnimplementedError: 2 root error(s) found. (0) Unimplemented: Cast string to float is not supported [[node functional_11/Cast (defined at :1) ]] (1) Cancelled: Function was cancelled before it was started 0 successful operations. 0 derived errors ignored. [Op:__inference_train_function_47870]

Function call stack: train_function -> train_function

Here is my code. Any idea what wrong I am doing ?

lstm_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(lstm_units, dropout=0.2, recurrent_dropout=0.2))

# loading our matrix
emb = tf.keras.layers.Embedding(max_words, embedding_dim, input_length=300, weights=[embedding_matrix],trainable=False)

input1 = tf.keras.Input(shape=(300,))
e1 = emb(input1)
x1 = lstm_layer(e1)

input2 = tf.keras.Input(shape=(300,))
e2 = emb(input2)
x2 = lstm_layer(e2)

mhd = lambda x: tf.keras.backend.abs(x[0] - x[1])

merged = tf.keras.layers.Lambda(function=mhd, output_shape=lambda x: x[0],name='L1_distance')([x1, x2])

preds = tf.keras.layers.Dense(1, activation='sigmoid')(merged)
model = tf.keras.Model(inputs=[input1, input2], outputs=preds)
model.compile(loss='mse', optimizer='adam')
model.summary()
history = model.fit([X_train[:,0], X_train[:,1]], y_train, epochs=100, validation_data=([X_valid[:,0], X_valid[:,1]], y_valid))```