switch for best & normal CF

hw6/Model.py

@@ -5,25 +5,38 @@
 from keras import backend as K
 from keras.regularizers import l2
 
-def build_cf_model(n_users, n_movies, dim):
+def build_cf_model(n_users, n_movies, dim, isBest=False):
  u_input = Input(shape=(1,))
- u = Embedding(n_users, dim, embeddings_regularizer=l2(1e-5))(u_input)
+ if isBest:
+ u = Embedding(n_users, dim, embeddings_regularizer=l2(1e-5))(u_input)
+ else:
+ u = Embedding(n_users, dim)(u_input)
  u = Reshape((dim,))(u)
  u = Dropout(0.1)(u)
 
  m_input = Input(shape=(1,))
- m = Embedding(n_movies, dim, embeddings_regularizer=l2(1e-5))(m_input)
+ if isBest:
+ m = Embedding(n_movies, dim, embeddings_regularizer=l2(1e-5))(m_input)
+ else:
+ m = Embedding(n_movies, dim)(m_input)
  m = Reshape((dim,))(m)
  m = Dropout(0.1)(m)
 
- u_bias = Embedding(n_users, 1, embeddings_regularizer=l2(1e-5))(u_input)
+ if isBest:
+ u_bias = Embedding(n_users, 1, embeddings_regularizer=l2(1e-5))(u_input)
+ else:
+ u_bias = Embedding(n_users, 1)(u_input)
  u_bias = Reshape((1,))(u_bias)
- m_bias = Embedding(n_movies, 1, embeddings_regularizer=l2(1e-5))(m_input)
+ if isBest:
+ m_bias = Embedding(n_movies, 1, embeddings_regularizer=l2(1e-5))(m_input)
+ else:
+ m_bias = Embedding(n_movies, 1)(m_input)
  m_bias = Reshape((1,))(m_bias)
 
  out = dot([u, m], -1)
  out = add([out, u_bias, m_bias])
- out = Lambda(lambda x: x + K.constant(3.581712))(out)
+ if isBest:
+ out = Lambda(lambda x: x + K.constant(3.581712))(out)
 
  model = Model(inputs=[u_input, m_input], outputs=out)
  return model