why is this model giving me a "Value Error" - numpy

Question

Its giving me a value error that I don't understand. Here is what it says :
Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays" my data has 8 columns and i m trying to predict the last 2 for output.
This is a ranking algorithm that I'm experimenting with my own data with :
import pandas as pd
import keras
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from keras import backend
from keras.layers import Activation, Dense, Input, Subtract
from keras.models import Model
INPUT_DIM = 7
# Model.
h_1 = Dense(128, activation="relu")
h_2 = Dense(64, activation="relu")
h_3 = Dense(32, activation="relu")
s = Dense(1)
# Relevant document score.
rel_doc = Input(shape=(INPUT_DIM,), dtype="float32")
h_1_rel = h_1(rel_doc)
h_2_rel = h_2(h_1_rel)
h_3_rel = h_3(h_2_rel)
rel_score = s(h_3_rel)
# Irrelevant document score.
irr_doc = Input(shape=(INPUT_DIM,), dtype="float32")
h_1_irr = h_1(irr_doc)
h_2_irr = h_2(h_1_irr)
h_3_irr = h_3(h_2_irr)
irr_score = s(h_3_irr)
# Subtract scores.
diff = Subtract()([rel_score, irr_score])
# Pass difference through sigmoid function.
prob = Activation("sigmoid")(diff)
# Build model.
model = Model(inputs=[rel_doc, irr_doc], outputs=prob)
model.compile(optimizer="adadelta", loss="binary_crossentropy")
# data.
data=pd.read_csv('ranking_dataset_remastered.csv')
print (data.head())
X = data.iloc[:, 1:7]
y = data.iloc[:, 6:7]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size =
0.2)
)
# Train model.
NUM_EPOCHS = 20
BATCH_SIZE = 512
history = model.fit(X_train, y_train, batch_size=BATCH_SIZE,
epochs=NUM_EPOCHS, verbose=1)
# Generate scores from document/query features.
get_score = backend.function([rel_doc], [rel_score])
get_score([X_train])
get_score([y_train])

Answer 1

When you defined your model with this line:
model = Model(inputs=[rel_doc, irr_doc], outputs=prob)
You created what keras refers to as a multi-input model, which essentially means that your model is expecting more than one inputs (in your case 2: rel_doc and irr_doc).
However during training you are just passing 1 input, X_train:
history = model.fit(X_train, y_train, batch_size=BATCH_SIZE,
epochs=NUM_EPOCHS, verbose=1)
What you should do in order to work is to have two arrays, one representing relevant and one irrelevant documents and feed them both to the model during training like this:
history = model.fit([X_rel_train, X_irr_train], y_train, batch_size=BATCH_SIZE,
epochs=NUM_EPOCHS, verbose=1)