def build(self, input_shape):
"""
The only function that is overloaded from the layer class.
We Set bias to be trainable.
:param input_shape: input tensor shape
:return: none
"""
if self.lambda_single:
self.scalar = self.add_weight(
shape=(1, ),
name="lambda",
initializer="ones",
dtype="float32",
trainable=True,
constraint=non_neg(),
)
else:
self.scalar = self.add_weight(
shape=(self.num_conv, ),
name="lambda",
initializer="ones",
dtype="float32",
trainable=True,
constraint=non_neg(),
)
self.built = True
def __init__(self, latent_dim=100, nb_rows=28, nb_columns=28, nb_input_channels=1, one_channel_output=True, dropout_rate=None):
"""
Create and initialize an autoencoder.
"""
self.latent_dim = latent_dim
self.nb_input_channels=nb_input_channels
self.nb_rows=nb_rows
self.nb_columns=nb_columns
if one_channel_output:
self.nb_output_channels=1
else:
self.nb_output_channels=nb_input_channels
input_img = Input(shape=(self.nb_rows, self.nb_columns, nb_input_channels)) # adapt this if using `channels_first` image data format
x = Flatten()(input_img)
encoded = Dense(latent_dim, activation='sigmoid')(x)
self.encoder = Model(input_img, encoded, name='encoder')
encoded_img = Input(shape=(self.latent_dim,))
if dropout_rate is None:
x = MaxPlusDense(self.nb_rows*self.nb_columns*self.nb_output_channels, use_bias=False,
kernel_constraint=constraints.non_neg())(encoded_img)
else:
x = Dropout(dropout_rate)(encoded_img)
x = MaxPlusDense(self.nb_rows*self.nb_columns*self.nb_output_channels, use_bias=False,
kernel_constraint=constraints.non_neg())(x)
decoded = Reshape((self.nb_rows,self.nb_columns,self.nb_output_channels))(x)
self.decoder = Model(encoded_img, decoded, name='decoder')
encoded = self.encoder(input_img)
decoded = self.decoder(encoded)
self.autoencoder = Model(input_img, decoded)
self.autoencoder.compile(optimizer='adadelta', loss='mean_squared_error', metrics=['mse'])
def Recmand_model(num_user, num_item, k):
input_uer = Input(shape=[
None,
], dtype="int32")
model_uer = Embedding(
num_user + 1,
k,
input_length=1,
embeddings_regularizer=regularizers.l2(0.001), #正则，下同
embeddings_constraint=non_neg() #非负，下同
)(input_uer)
model_uer = Dense(k, activation="relu", use_bias=True)(model_uer) #激活函数
model_uer = Dropout(0.1)(model_uer) #Dropout 随机删去一些节点，防止过拟合
model_uer = Reshape((k, ))(model_uer)
input_item = Input(shape=[
None,
], dtype="int32")
model_item = Embedding(num_item + 1,
k,
input_length=1,
embeddings_regularizer=regularizers.l2(0.001),
embeddings_constraint=non_neg())(input_item)
model_item = Dense(k, activation="relu", use_bias=True)(model_item)
model_item = Dropout(0.1)(model_item)
model_item = Reshape((k, ))(model_item)
out = Dot(1)([model_uer, model_item]) #点积运算
model = Model(inputs=[input_uer, input_item], outputs=out)
model.compile(loss='mse', optimizer='Adam')
model.summary()
return model
def NMF_image(n_users, n_items, n_factors):
item_input = Input(shape=[1])
item_embedding = Embedding(n_items,
n_factors,
embeddings_regularizer=l2(1e-5),
embeddings_constraint=non_neg())(item_input)
item_vec = Flatten()(item_embedding)
image_input = Input(shape=(224, 224, 3))
imgflow = tf.keras.layers.Conv2D(32, (3, 3),
padding='same',
activation='relu')(image_input)
imgflow = tf.keras.layers.Conv2D(32, (3, 3),
padding='same',
activation='relu')(imgflow)
imgflow = MaxPooling2D(pool_size=(4, 4))(imgflow)
imgflow = Dropout(0.25)(imgflow)
imgflow = tf.keras.layers.Conv2D(32, (3, 3),
padding='same',
activation='relu')(imgflow)
imgflow = MaxPooling2D(pool_size=(4, 4))(imgflow)
imgflow = Dropout(0.25)(imgflow)
imgflow = tf.keras.layers.Conv2D(32, (3, 3),
padding='same',
activation='relu')(imgflow)
imgflow = Dropout(0.25)(imgflow)
imgflow = tf.keras.layers.Conv2D(32, (3, 3),
padding='same',
activation='relu')(imgflow)
imgflow = Dropout(0.25)(imgflow)
imgflow = Flatten()(imgflow)
imgflow = Dense(512, activation='relu')(imgflow)
imgflow = BatchNormalization()(imgflow)
imgflow = Dense(256, activation='relu')(imgflow)
imgflow = BatchNormalization()(imgflow)
imgflow = Dense(128, activation='relu')(imgflow)
Concat_1 = tf.keras.layers.concatenate(inputs=[item_vec, imgflow], axis=1)
Concat_1 = Dense(n_factors, kernel_initializer='glorot_normal')(Concat_1)
user_input = Input(shape=[1])
user_embedding = Embedding(n_users,
n_factors,
embeddings_regularizer=l2(1e-5),
embeddings_constraint=non_neg())(user_input)
user_vec = Flatten()(user_embedding)
DotProduct = Dot(axes=1)([Concat_1, user_vec])
model = Model([user_input, item_input, image_input], DotProduct)
model.compile(loss='mean_squared_error', optimizer="adam")
return model
def gen_model(n_users, n_items, latent_dim, normalize):
userInputLayer = layers.Input(shape=[1])
itemInputLayer = layers.Input(shape=[1])
if normalize is True:
userVec = layers.Embedding(n_users,
latent_dim,
embeddings_initializer='random_normal',
name='User_Embedding')(userInputLayer)
itemVec = layers.Embedding(n_items,
latent_dim,
embeddings_initializer='random_normal',
name='Movie_Embedding')(itemInputLayer)
else: #non-negative matrix
userVec = layers.Embedding(
n_users,
latent_dim,
embeddings_initializer='random_normal',
name='User_Embedding',
embeddings_constraint=non_neg())(userInputLayer)
itemVec = layers.Embedding(
n_items,
latent_dim,
embeddings_initializer='random_normal',
name='Movie_Embedding',
embeddings_constraint=non_neg())(itemInputLayer)
userBias = layers.Embedding(n_users, 1,
embeddings_initializer='zeros')(userInputLayer)
itemBias = layers.Embedding(n_items, 1,
embeddings_initializer='zeros')(itemInputLayer)
userVec = layers.Flatten()(userVec)
userBias = layers.Flatten()(userBias)
itemVec = layers.Flatten()(itemVec)
itemBias = layers.Flatten()(itemBias)
r_hat = layers.Dot(name='Dot', axes=1)([userVec, itemVec])
r_hat = layers.Add(name='Bias')([r_hat, userBias, itemBias])
#outputLayer = layers.Concatenate()([inputLayer_a, inputLayer_b])
#keras.layers.Concatenate(axis=-1)
model = models.Model(inputs=[userInputLayer, itemInputLayer],
outputs=r_hat)
model.summary()
model.compile(loss='mse', optimizer='adam')
plot_model(model,
to_file='tmp/model.png',
show_shapes=True,
show_layer_names=True)
return model
def get_model(self): item_input = Input(shape=[1], name='Item') item_embedding = Embedding(self.num_tweets, self.n_latent_factors_item, name='Item-Embedding', embeddings_constraint=non_neg())(item_input) item_vec = Flatten(name='FlattenItem')(item_embedding) user_input = Input(shape=[1], name='User') user_vec = Flatten(name='FlattenUsers')( Embedding(self.num_users, self.n_latent_factors_user, name='User-Embedding', embeddings_constraint=non_neg())(user_input)) prod = dot([item_vec, user_vec], axes=1, name='DotProduct') return Model(input=[user_input, item_input], output=prod)
def build(self, input_shape):
# Set the input dimensions as the output dimension of the conv layer
assert len(input_shape) >= 2
self.num_data = input_shape[0]
self.input_dim = self.tied_layer.output_shape[-1]
self.input_spec = [InputSpec(min_ndim=2, axes={-1: self.input_dim})]
# Set kernel from the tied layer
self.kernel = K.reverse(self.tied_layer.kernel, axes=0)
self.kernel = K.reverse(self.kernel, axes=1)
self.kernel = K.reshape(
self.kernel,
(
self.kernel_size[0],
self.kernel_size[1],
self.tied_layer.output_shape[-1],
1,
),
)
# Set bias from the lambda_value
if self.lambda_single:
self.bias = self.add_weight(
shape=(1, ),
initializer=self.bias_initializer,
name="lambda",
regularizer=self.bias_regularizer,
trainable=self.lambda_trainable,
constraint=non_neg(),
)
else:
self.bias = self.add_weight(
shape=(self.tied_layer.output_shape[3], ),
initializer=self.bias_initializer,
name="lambda",
regularizer=self.bias_regularizer,
trainable=self.lambda_trainable,
constraint=non_neg(),
)
# noiseSTD
self.noiseSTD = self.add_weight(
shape=(1, ),
initializer=self.bias_initializer,
name="noiseSTD",
regularizer=self.bias_regularizer,
trainable=False,
constraint=non_neg(),
)
# Have to set build to True
self.built = True
def build(self, input_shape):
# Create a trainable weight variable for this layer.
self.wp = self.add_weight(name='positive_weights',
shape=(input_shape[1], self.output_dim),
initializer=RandomUniform(minval=0,
maxval=0.2),
trainable=True,
constraint=contraints.non_neg())
self.wn = self.add_weight(name='negative_weights',
shape=(input_shape[1], self.output_dim),
initializer=RandomUniform(minval=0,
maxval=0.2),
trainable=True,
constraint=contraints.non_neg())
super(RandomLayer, self).build(input_shape)
def build_stream(monotonic):
nfts = self.num_monotonic if monotonic else self.num_unconstrained
input_ = KL.Input((self.num_features,))
n = self.num_unconstrained
if monotonic:
last_ = KL.Lambda(lambda x: x[:, n:],)(input_)
else:
last_ = KL.Lambda(lambda x: x[:, : n],)(input_)
if nfts > 0:
constraint = non_neg() if monotonic else None
num_dense = self.dense_monotonic_num if monotonic else \
self.dense_unconstrained_num
for d in range(num_dense):
last_ = KL.Dense(self.dense_width,
activation=self.dense_activation,
use_bias=True,
kernel_constraint=constraint,
#kernel_regularizer=l2(self.l2),
)(last_)
if self.dropout is not None and self.dropout > 0.:
last_ = KL.Dropout(self.dropout)(last_)
submodel = Model([input_], [last_])
#submodel.summary()
return submodel
def train_network_convex(self):
x_train = (self.df_price - 50) / 100
y_train = 90 - self.revenue
model = Sequential()
model.add(Dense(units=100, input_dim=self.product_number))
model.add(Activation("sigmoid"))
#add the constraint: kernel_constraint =non_neg() to ensure its convexity
model.add(Dense(units=1, kernel_constraint=non_neg()))
model.add(Activation("linear"))
model.summary()
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
model.fit(x_train,
y_train,
validation_split=0.2,
epochs=300,
batch_size=32)
price_new = (np.random.randn(10, 10) * 10 + 25) / 100
# s = model.predict(price_new)
# print(s)
self.weights = np.array(model.get_weights())
self.weights[0] = self.weights[0].T
price = np.ones(self.product_number)
s = np.array(
[self.weights[2][i][0] for i in range(len(self.weights[2]))])
def test_some_pairwise(self):
feature_names = [
"f1", "f2", "f3", "f5", "f6", "f7", "f8", "f9", "f10", "f11"
]
fm = DeepFM(model_features=[["f1"], ["f2"], [10], [10], [10], [10],
[10], [10], [10], [10]],
feature_dimensions=[
100, 1, 100, 100, 100, 100, 100, 100, 100, 100
],
realval=[
False, True, False, False, False, False, False, False,
False, False
],
mask_zero=True,
feature_names=feature_names,
obj="nce")
groups = zip(["f1"] * (len(feature_names) - 1), feature_names[1:])
print(groups)
model = fm.build_model(10,
dropout_layer=0.5,
deep_out=True,
deep_out_bias=False,
deep_weight_groups=[groups],
deep_kernel_constraint=non_neg())
try:
from keras.utils import plot_model
plot_model(model, to_file="some_pairwise.png")
except:
pass
def __init__(self, latent_dim=100, nb_rows=28, nb_columns=28, nb_input_channels=1, one_channel_output=True,
sparsity_weight=0.1, sparsity_objective=0.1):
"""
Create a sparse shallow AE with the custom kl divergence regularizer, enforcing weights non negativity with Keras NonNeg constraint.
Arguments:
sparsity_weight: positive float - the weight of the sparsity cost.
sparsity_objective: float between 0 and 1 - the sparsity parameter.
"""
self.latent_dim = latent_dim
self.nb_rows=nb_rows
self.nb_columns=nb_columns
self.nb_input_channels=nb_input_channels
if one_channel_output:
self.nb_output_channels=1
else:
self.nb_output_channels=nb_input_channels
self.sparsity_weight = sparsity_weight
self.sparsity_objective = sparsity_objective
input_img = Input(shape=(self.nb_rows, self.nb_columns, nb_input_channels)) # adapt this if using `channels_first` image data format
x = Flatten()(input_img)
encoded = Dense(latent_dim, activation='sigmoid',
activity_regularizer=custom_regularizers.KL_divergence_sum(beta=self.sparsity_weight,
rho=self.sparsity_objective))(x)
self.encoder = Model(input_img, encoded, name='encoder')
encoded_img = Input(shape=(self.latent_dim,))
x = Dense(self.nb_rows*self.nb_columns*self.nb_output_channels,
kernel_constraint=constraints.non_neg())(encoded_img)
x = LeakyReLU(alpha=0.1)(x)
decoded = Reshape((self.nb_rows,self.nb_columns,self.nb_output_channels))(x)
self.decoder = Model(encoded_img, decoded, name='decoder')
encoded = self.encoder(input_img)
decoded = self.decoder(encoded)
self.autoencoder = Model(input_img, decoded)
self.autoencoder.compile(optimizer='adadelta', loss='mean_squared_error', metrics=['mse'])
def convex_model2(input_dim, output_dim):
input = keras.layers.Input(shape=(input_dim, ))
x0 = keras.layers.Dense(150,
kernel_constraint=non_neg(),
activation='relu')(input)
x1 = keras.layers.Dense(150,
kernel_constraint=non_neg(),
activation='relu')(x0)
direct1 = keras.layers.Dense(150, activation='relu')(input)
x2 = keras.layers.Add()([x1, direct1])
x2 = keras.layers.Dense(30, kernel_constraint=non_neg(),
activation='relu')(x2)
out = keras.layers.Dense(output_dim, kernel_constraint=non_neg())(x2)
model = keras.models.Model(inputs=input, outputs=out)
return model
def build(self, input_shape):
# Create a trainable weight variable for this layer.
self.W = self.add_weight(name='highway',
shape=(1, self.output_dim),
initializer='uniform',
constraint=non_neg(),
trainable=True)
super(HighwayWeights, self).build(input_shape) # Be sure to call this at the end
def get_model(self):
item_input = Input(shape=[1], name='Item')
item_embedding = Embedding(self.num_tweets,
self.n_latent_factors_item,
name='Item-Embedding',
embeddings_constraint=non_neg())(item_input)
item_vec = Flatten(name='FlattenItem')(item_embedding)
user_input = Input(shape=[1], name='User')
user_vec = Flatten(name='FlattenUsers')(Embedding(
self.num_users,
self.n_latent_factors_user,
name='User-Embedding',
embeddings_constraint=non_neg())(user_input))
prod = dot([item_vec, user_vec], axes=1, name='DotProduct')
return Model(input=[user_input, item_input], output=prod)
def create_model(self, n_users, n_items):
movie_input = keras.layers.Input(shape=[1], name='Item')
movie_embedding = keras.layers.Embedding(
n_items + 1, self.n_latent_factors_movie,
name='Movie-Embedding')(movie_input)
if (self.nonneg):
movie_embedding = keras.layers.Embedding(
n_items + 1,
self.n_latent_factors_movie,
name='Movie-Embedding',
embeddings_constraint=non_neg())(movie_input)
movie_vec = keras.layers.Flatten(name='FlattenMovies')(movie_embedding)
movie_vec = keras.layers.Dropout(0.2)(movie_vec)
user_input = keras.layers.Input(shape=[1], name='User')
user_vec = keras.layers.Flatten(name='FlattenUsers')(
keras.layers.Embedding(n_users + 1,
self.n_latent_factors_user,
name='User-Embedding')(user_input))
if (self.nonneg):
user_vec = keras.layers.Flatten(name='FlattenUsers')(
keras.layers.Embedding(
n_users + 1,
self.n_latent_factors_user,
name='User-Embedding',
embeddings_constraint=non_neg())(user_input))
user_vec = keras.layers.Dropout(0.2)(user_vec)
concat = keras.layers.concatenate([movie_vec, user_vec], name='Concat')
concat_dropout = keras.layers.Dropout(0.2)(concat)
dense = keras.layers.Dense(200, name='FullyConnected')(concat)
dropout_1 = keras.layers.Dropout(0.2, name='Dropout')(dense)
dense_2 = keras.layers.Dense(100, name='FullyConnected-1')(concat)
dropout_2 = keras.layers.Dropout(0.2, name='Dropout')(dense_2)
dense_3 = keras.layers.Dense(50, name='FullyConnected-2')(dense_2)
dropout_3 = keras.layers.Dropout(0.2, name='Dropout')(dense_3)
dense_4 = keras.layers.Dense(20,
name='FullyConnected-3',
activation='relu')(dense_3)
result = keras.layers.Dense(1, activation='relu',
name='Activation')(dense_4)
adam = Adam(lr=0.005)
self.model = keras.Model([user_input, movie_input], result)
self.model.compile(optimizer=adam, loss='mean_absolute_error')
def _create_embedding_user(self):
self.user_input = keras.layers.Input(shape=[1], name='User')
self.user_vec = keras.layers.Flatten(name='FlattenUsers')(
keras.layers.Embedding(self.n_user + 1,
self.n_latent_ftr,
name='User-Embedding',
embeddings_constraint=non_neg())(
self.user_input))
self.user_vec = keras.layers.Dropout(0.2)(self.user_vec)
def build(self, input_shape):
nb_feats = input_shape[-1]
std_shape = (1, 1, 1, nb_feats)
self.min_std = self.add_weight(shape=std_shape,
initializer=Constant(self.min_std_val),
name='min_std',
constraint=non_neg())
self.built = True
return
def build(self, unit, input_shape=None):
self.prop_weights = self.add_weight(
name='proportion-weights',
shape=(unit,),
initializer=RND_UNI,
trainable=True,
constraint=non_neg()
)
super(Multiply, self).build(input_shape)
def normal_model(input_dim, output_dim):
input = keras.layers.Input(shape=(input_dim, ))
x0 = keras.layers.Dense(80, activation='relu')(input)
x1 = keras.layers.Dense(50, activation='relu')(x0)
x2 = keras.layers.Dense(30, activation='relu')(x1)
out = keras.layers.Dense(output_dim, kernel_constraint=non_neg())(x2)
model = keras.models.Model(inputs=input, outputs=out)
return model
def arch8():
model = Sequential()
model.add(Dense(512, input_dim=122, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(32, activation='sigmoid'))
model.add(Dropout(0.3))
model.add(Dense(1, kernel_constraint=non_neg(), activation='sigmoid'))
return model
def build(self, input_shape):
assert isinstance(input_shape, list)
# Create a trainable weights variable for this layer.
self.kernel1 = self.add_weight(name="modality_weight_1",
shape=(1, ),
initializer=constant(value=0.0),
trainable=True,
constraint=non_neg())
super(Linear, self).build(input_shape)
def create_model(self, n_users, n_items):
user_id_input = keras.layers.Input(shape=[1], name='user')
item_id_input = keras.layers.Input(shape=[1], name='item')
user_embedding = keras.layers.Embedding(
output_dim=100,
input_dim=n_users + 1,
input_length=1,
name='user_embedding')(user_id_input)
if (self.nonneg):
user_embedding = keras.layers.Embedding(
output_dim=100,
input_dim=n_users + 1,
input_length=1,
name='user_embedding',
embeddings_constraint=non_neg())(user_id_input)
item_embedding = keras.layers.Embedding(
output_dim=100,
input_dim=n_items + 1,
input_length=1,
name='item_embedding')(item_id_input)
if (self.nonneg):
item_embedding = keras.layers.Embedding(
output_dim=100,
input_dim=n_items + 1,
input_length=1,
name='item_embedding',
embeddings_constraint=non_neg())(item_id_input)
user_vecs = keras.layers.Flatten()(user_embedding)
item_vecs = keras.layers.Flatten()(item_embedding)
user_dropout = keras.layers.Dropout(0.2,
name="user_dropout")(user_vecs)
item_dropout = keras.layers.Dropout(0.2,
name="item_dropout")(item_vecs)
y = keras.layers.dot([user_dropout, item_dropout], axes=1)
self.model = keras.models.Model(inputs=[user_id_input, item_id_input],
outputs=[y])
self.model.compile(optimizer='adam', loss='mae')
def arch8():
model = Sequential()
model.add(Dense(512, input_dim=122, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(32, activation='sigmoid'))
model.add(Dropout(0.3))
model.add(Dense(1, kernel_constraint=non_neg(), activation='sigmoid'))
return model
def _create_embedding_item(self):
self.item_input = keras.layers.Input(shape=[1], name='Item')
self.item_embedding = keras.layers.Embedding(
self.n_item + 1,
self.n_latent_ftr,
name='Item-Embedding',
embeddings_constraint=non_neg())(self.item_input)
self.item_vec = keras.layers.Flatten(name='FlattenItems')(
self.item_embedding)
self.item_vec = keras.layers.Dropout(0.2)(self.item_vec)
def build(self, input_shape):
assert len(input_shape[0]) >= 2
input_dim = input_shape[0][-1]
self.mu = self.add_weight(shape=(self.num_comp, input_dim),
initializer=initializers.TruncatedNormal(
mean=0.0, stddev=0.1),
name='mu')
self.s = self.add_weight(shape=(self.num_comp, ),
initializer='ones',
name='s',
constraint=constraints.non_neg())
def __init__(self,
alpha_initializer='ones',
alpha_regularizer=None,
alpha_constraint=constraints.non_neg(),
beta_initializer='ones',
beta_regularizer=None,
beta_constraint=constraints.non_neg(),
shared_axes=None,
**kwargs):
super(PTReLU, self).__init__(**kwargs)
self.supports_masking = True
self.alpha_initializer = initializers.get(alpha_initializer)
self.alpha_regularizer = regularizers.get(alpha_regularizer)
self.alpha_constraint = constraints.get(alpha_constraint)
self.beta_initializer = initializers.get(beta_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
if shared_axes is None:
self.shared_axes = None
elif not isinstance(shared_axes, (list, tuple)):
self.shared_axes = [shared_axes]
else:
self.shared_axes = list(shared_axes)
def build(self, input_shape):
# Create trainable weights for this layer.
# This layer simulates a mosaic function by applying a per-pixel dot product on input channels.
# The total number of weights is equal to the input image shape (img_width * img_height * img_channels)
print("input_shape: " + str(input_shape))
self.cfa = self.add_weight(name='colorFilter',
shape=(input_shape[1], input_shape[2],
input_shape[3]),
initializer=constant(value=0.5),
trainable=True,
constraint=non_neg())
super(MosaicLayer, self).build(input_shape)
def test_all_pairwise(self):
feature_names = [
"f1", "f2", "f3", "f5", "f6", "f7", "f8", "f9", "f10", "f11"
]
fm = DeepFM(model_features=[[
"f1",
], ["f2"], [1, 2], [1, 2], [1, 2], [1, 2], [1, 2], [1, 2], [1, 2],
[1, 2]],
feature_dimensions=[
100, 1, 100, 100, 100, 100, 100, 100, 100, 100
],
realval=[
False, True, False, False, False, False, False, False,
False, False
],
mask_zero=True,
feature_names=feature_names,
obj="ns")
model = fm.build_model(10,
dropout_layer=0.5,
deep_out=True,
deep_out_bias=False,
deep_kernel_constraint=non_neg())
model.compile(loss='binary_crossentropy',
metrics=['accuracy'],
optimizer=tf.train.AdamOptimizer())
model.fit(x=[
np.array([0]),
np.array([0]),
np.array([[51, 2]]),
np.array([[0, 0]]),
np.array([[25, 1]]),
np.array([[0, 0]]),
np.array([[17, 1]]),
np.array([[1, 1]]),
np.array([[1, 1]]),
np.array([[0, 0]])
],
y=np.array([0]))
try:
from keras.utils import plot_model
plot_model(model, to_file="all_pairwise.png")
except:
pass
def build(self, input_shape):
"""
The only function that is overloaded from the Dense layer class.
We Set bias to be trainable.
:param input_shape: input tensor shape
:return: none
"""
# Create a trainable weight variable for this layer.
self.bias = self.add_weight(
shape=(self.num_conv, ),
initializer=self.bias_initializer,
name="bias",
regularizer=self.bias_regularizer,
trainable=True,
constraint=non_neg(),
)
self.built = True
def __init__(self,
latent_dim=100,
nb_rows=28,
nb_columns=28,
nb_input_channels=1,
one_channel_output=True):
"""
Create a shallow AE with the Keras Non Negativity Constraint.
"""
self.latent_dim = latent_dim
self.nb_input_channels = nb_input_channels
self.nb_rows = nb_rows
self.nb_columns = nb_columns
if one_channel_output:
self.nb_output_channels = 1
else:
self.nb_output_channels = nb_input_channels
input_img = Input(
shape=(self.nb_rows, self.nb_columns, self.nb_input_channels
)) # adapt this if using `channels_first` image data format
x = Conv2D(64, (4, 4), strides=(2, 2), padding='same')(input_img)
x = LeakyReLU(alpha=0.1)(x)
x = Conv2D(128, (4, 4), strides=(2, 2), padding='same')(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.1)(x)
x = Flatten()(x)
x = Dense(1024)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.1)(x)
encoded = Dense(self.latent_dim, activation='sigmoid')(x)
self.encoder = Model(input_img, encoded, name='encoder')
encoded_img = Input(shape=(self.latent_dim, ))
x = Dense(self.nb_rows * self.nb_columns * self.nb_output_channels,
kernel_constraint=constraints.non_neg())(encoded_img)
x = LeakyReLU(alpha=0.1)(x)
decoded = Reshape(
(self.nb_rows, self.nb_columns, self.nb_output_channels))(x)
self.decoder = Model(encoded_img, decoded, name='decoder')
encoded = self.encoder(input_img)
decoded = self.decoder(encoded)
self.autoencoder = Model(input_img, decoded)
self.autoencoder.compile(optimizer='adadelta',
loss='mean_squared_error',
metrics=['mse'])
def make_model(self):
x = Input(shape=(self.look_back, ))
ar_output = Dense(units=1,
kernel_initializer='uniform',
kernel_constraint=unit_norm(),
name='ar-weights')(x)
pre_point = Lambda(lambda k: k[:, -1:])(x)
merged_output = concatenate([ar_output, pre_point])
outputs = Dense(units=1,
kernel_initializer=RND_UNI,
use_bias=False,
kernel_constraint=non_neg(),
name='contrib-weights')(merged_output)
model = Model(inputs=x, outputs=outputs)
model.compile('Adam', 'mae')
return model
def test_non_neg():
non_neg_instance = constraints.non_neg()
normed = non_neg_instance(K.variable(get_example_array()))
assert(np.all(np.min(K.eval(normed), axis=1) == 0.))
rate_num = len(rate)
user_num = len(user_dict)
book_num = len(book_dict)
if args.use_implicit:
feedback_u, feedback_b = get_feedback(user_all, book_all, user_num, book_num)
feedback_u, feedback_b = pad_sequences(feedback_u), pad_sequences(feedback_b)
print('Data prepared')
# Model
u_input = Input(shape=[1], name='user')
if not args.nmf:
U = Embedding(user_num, args.emb_dim, input_length=1, embeddings_initializer="random_normal", name='user_embed')(u_input)
else:
U = Embedding(user_num, args.emb_dim, input_length=1, embeddings_initializer="random_normal", embeddings_constraint=non_neg(), name='user_embed')(u_input)
U = Dropout(0.3)(U)
U = Flatten()(U)
b_input = Input(shape=[1], name='book')
if not args.nmf:
B = Embedding(book_num, args.emb_dim, input_length=1, embeddings_initializer="random_normal", name='book_embed')(b_input)
else:
B = Embedding(book_num, args.emb_dim, input_length=1, embeddings_initializer="random_normal", embeddings_constraint=non_neg(), name='book_embed')(b_input)
B = Dropout(0.3)(B)
B = Flatten()(B)
U_bias = Embedding(user_num, 1, input_length=1, embeddings_initializer="zeros", name='user_embed_bias')(u_input)
