Hi, Ray,

Thanks for the help! I have data like

train.head()

ProbeID words mrFastCount Slope

0 AX-169355813 tatg atga tgac gacc accc ccct cctg ctga tgaa g… 0.04 0.428

1 AX-16629260 gaca acat catt attt tttt tttt tttg ttgg tgga g… 0.10 0.613

2 AX-169451789 ctct tcta ctag tagc agcg gcgt cgtg gtgg tggg g… 0.01 0.535

3 AX-169436214 tgcc gcca ccaa caat aatg atgc tgca gcat catg a… 0.05 0.235

4 AX-169450602 tcag cagt agtt gttt tttc ttca tcaa caac aaca a… 0.01 0.439

train.shape

(76373, 4)

X is based on column words only. It will go through the RNN model.

X[0:3,]

array([[110, 34, 154, 207, 209, 191, 79, 55, 20, 177, 71, 165, 195,

97, 7, 177, 28, 39, 18, 12, 44, 100, 155, 38, 152, 83,

59],

[128, 40, 31, 5, 3, 3, 17, 96, 48, 87, 159, 77, 183,

86, 66, 104, 80, 21, 52, 65, 136, 176, 143, 119, 101, 27,

102],

[ 61, 105, 195, 196, 225, 244, 213, 131, 85, 122, 203, 128, 59,

32, 49, 202, 67, 37, 113, 73, 193, 215, 235, 216, 141, 187,

146]], dtype=int32)

X.shape

(76373, 27)

train_y = np.array(train.Slope)

max_length=27

embedding_dim = 10

gru_dim=12

dense_dim = 6

NUM_EPOCHS = 50

BATCH_SIZE=128

#model architecture*****

in1 = tf.keras.layers.Input(shape=(max_length,))

in2 = tf.keras.layers.Input(shape=(1,)) # for the known variable --mrFastCount I want to add in the concatenate layer.

x = tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length)(in1)

x = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(gru_dim,dropout=0.4))(x)

x = tf.keras.layers.Dense(dense_dim, activation=‘relu’)(x)

x = tf.keras.layers.concatenate()([x, in2])

out = tf.keras.layers.Dense(1)(x)

model_gru = tf.keras.models.Model(inputs=[in1, in2], outputs=out)

#
Set the training parameters

optimizer=tf.keras.optimizers.RMSprop(0.001)

model_gru.compile(loss=tf.keras.losses.Huber(), optimizer=optimizer, metrics=[‘mae’])

#
Print the model summary

model_gru.summary()

#
Train the model

history_gru = model_gru.fit([X, train.mrFastCount], train_y, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS, validation_data=([val_padded, valid.mrFastCount], val_y))

#
I got the following error:

ValueError: Layer “model” expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor ‘IteratorGetNext:0’ shape=(None, 27) dtype=int32>]

What is wrong? Thanks