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updated for keras2.1.2 and tensorflow1.4.1 #11

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updated for keras2.1.2 and tensorflow1.4.1 #11

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17 changes: 9 additions & 8 deletions GMF.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
import theano.tensor as T
import keras
from keras import backend as K
from keras import initializations
from keras import initializers
from keras.models import Sequential, Model, load_model, save_model
from keras.layers.core import Dense, Lambda, Activation
from keras.layers import Embedding, Input, Dense, merge, Reshape, Merge, Flatten
Expand Down Expand Up @@ -51,18 +51,19 @@ def parse_args():
help='Whether to save the trained model.')
return parser.parse_args()

def init_normal(shape, name=None):
return initializations.normal(shape, scale=0.01, name=name)
#def init_normal(shape, name=None):
# return initializations.normal(shape, scale=0.01, name=name)
# return initializers.normal()

def get_model(num_users, num_items, latent_dim, regs=[0,0]):
# Input variables
user_input = Input(shape=(1,), dtype='int32', name = 'user_input')
item_input = Input(shape=(1,), dtype='int32', name = 'item_input')

MF_Embedding_User = Embedding(input_dim = num_users, output_dim = latent_dim, name = 'user_embedding',
init = init_normal, W_regularizer = l2(regs[0]), input_length=1)
MF_Embedding_Item = Embedding(input_dim = num_items, output_dim = latent_dim, name = 'item_embedding',
init = init_normal, W_regularizer = l2(regs[1]), input_length=1)
# MF_Embedding_User = Embedding(input_dim = num_users, output_dim = latent_dim, name = 'user_embedding', init = init_normal, W_regularizer = l2(regs[0]), input_length=1)
MF_Embedding_User = Embedding(input_dim = num_users, output_dim = latent_dim, name = 'user_embedding', embeddings_initializer = 'random_normal', W_regularizer = l2(regs[0]), input_length=1)
# MF_Embedding_Item = Embedding(input_dim = num_items, output_dim = latent_dim, name = 'item_embedding', init = init_normal, W_regularizer = l2(regs[1]), input_length=1)
MF_Embedding_Item = Embedding(input_dim = num_items, output_dim = latent_dim, name = 'item_embedding', embeddings_initializer = 'random_normal', W_regularizer = l2(regs[1]), input_length=1)

# Crucial to flatten an embedding vector!
user_latent = Flatten()(MF_Embedding_User(user_input))
Expand Down Expand Up @@ -168,4 +169,4 @@ def get_train_instances(train, num_negatives):

print("End. Best Iteration %d: HR = %.4f, NDCG = %.4f. " %(best_iter, best_hr, best_ndcg))
if args.out > 0:
print("The best GMF model is saved to %s" %(model_out_file))
print("The best GMF model is saved to %s" %(model_out_file))
15 changes: 5 additions & 10 deletions MLP.py
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Expand Up @@ -12,9 +12,9 @@
import theano.tensor as T
import keras
from keras import backend as K
from keras import initializations
from keras.regularizers import l2, activity_l2
from keras.models import Sequential, Graph, Model
from keras import initializers
from keras.regularizers import l2
from keras.models import Sequential, Model
from keras.layers.core import Dense, Lambda, Activation
from keras.layers import Embedding, Input, Dense, merge, Reshape, Merge, Flatten, Dropout
from keras.constraints import maxnorm
Expand Down Expand Up @@ -53,20 +53,15 @@ def parse_args():
help='Whether to save the trained model.')
return parser.parse_args()

def init_normal(shape, name=None):
return initializations.normal(shape, scale=0.01, name=name)

def get_model(num_users, num_items, layers = [20,10], reg_layers=[0,0]):
assert len(layers) == len(reg_layers)
num_layer = len(layers) #Number of layers in the MLP
# Input variables
user_input = Input(shape=(1,), dtype='int32', name = 'user_input')
item_input = Input(shape=(1,), dtype='int32', name = 'item_input')

MLP_Embedding_User = Embedding(input_dim = num_users, output_dim = layers[0]/2, name = 'user_embedding',
init = init_normal, W_regularizer = l2(reg_layers[0]), input_length=1)
MLP_Embedding_Item = Embedding(input_dim = num_items, output_dim = layers[0]/2, name = 'item_embedding',
init = init_normal, W_regularizer = l2(reg_layers[0]), input_length=1)
MLP_Embedding_User = Embedding(input_dim = num_users, output_dim = layers[0]/2, name = 'user_embedding', embeddings_initializer = 'random_normal', W_regularizer = l2(reg_layers[0]), input_length=1)
MLP_Embedding_Item = Embedding(input_dim = num_items, output_dim = layers[0]/2, name = 'item_embedding', embeddings_initializer = 'random_normal', W_regularizer = l2(reg_layers[0]), input_length=1)

# Crucial to flatten an embedding vector!
user_latent = Flatten()(MLP_Embedding_User(user_input))
Expand Down
19 changes: 6 additions & 13 deletions NeuMF.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,8 @@
import theano.tensor as T
import keras
from keras import backend as K
from keras import initializations
from keras.regularizers import l1, l2, l1l2
from keras import initializers
from keras.regularizers import l1, l2
from keras.models import Sequential, Model
from keras.layers.core import Dense, Lambda, Activation
from keras.layers import Embedding, Input, Dense, merge, Reshape, Merge, Flatten, Dropout
Expand Down Expand Up @@ -59,9 +59,6 @@ def parse_args():
help='Specify the pretrain model file for MLP part. If empty, no pretrain will be used')
return parser.parse_args()

def init_normal(shape, name=None):
return initializations.normal(shape, scale=0.01, name=name)

def get_model(num_users, num_items, mf_dim=10, layers=[10], reg_layers=[0], reg_mf=0):
assert len(layers) == len(reg_layers)
num_layer = len(layers) #Number of layers in the MLP
Expand All @@ -70,15 +67,11 @@ def get_model(num_users, num_items, mf_dim=10, layers=[10], reg_layers=[0], reg_
item_input = Input(shape=(1,), dtype='int32', name = 'item_input')

# Embedding layer
MF_Embedding_User = Embedding(input_dim = num_users, output_dim = mf_dim, name = 'mf_embedding_user',
init = init_normal, W_regularizer = l2(reg_mf), input_length=1)
MF_Embedding_Item = Embedding(input_dim = num_items, output_dim = mf_dim, name = 'mf_embedding_item',
init = init_normal, W_regularizer = l2(reg_mf), input_length=1)
MF_Embedding_User = Embedding(input_dim = num_users, output_dim = mf_dim, name = 'mf_embedding_user', embeddings_initializer = 'random_normal', W_regularizer = l2(reg_mf), input_length=1)
MF_Embedding_Item = Embedding(input_dim = num_items, output_dim = mf_dim, name = 'mf_embedding_item', embeddings_initializer = 'random_normal', W_regularizer = l2(reg_mf), input_length=1)

MLP_Embedding_User = Embedding(input_dim = num_users, output_dim = layers[0]/2, name = "mlp_embedding_user",
init = init_normal, W_regularizer = l2(reg_layers[0]), input_length=1)
MLP_Embedding_Item = Embedding(input_dim = num_items, output_dim = layers[0]/2, name = 'mlp_embedding_item',
init = init_normal, W_regularizer = l2(reg_layers[0]), input_length=1)
MLP_Embedding_User = Embedding(input_dim = num_users, output_dim = layers[0]/2, name = "mlp_embedding_user", embeddings_initializer = 'random_normal', W_regularizer = l2(reg_layers[0]), input_length=1)
MLP_Embedding_Item = Embedding(input_dim = num_items, output_dim = layers[0]/2, name = 'mlp_embedding_item', embeddings_initializer = 'random_normal', W_regularizer = l2(reg_layers[0]), input_length=1)

# MF part
mf_user_latent = Flatten()(MF_Embedding_User(user_input))
Expand Down