Comments (6)
@cynthia166 could you clean up the formatting of the above so it becomes more readable.
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Hello Miko,
I cleaned up the format:
My question is: How can i make the talos.Evaluate(scan_object) work I get an error becaus my x is multiple inputs, and the error says that list has no shape.
My code:
def autoEncoder(x_train, y_train, x_val, y_val, params):
'''
Autoencoder for Collaborative Filter Model
'''
#model = Sequential()
#users_items_matrix, content_info = X
#content_info = X[:,420:X.shape[1]]
#users_items_matrix = X[:,0:420]
# Input
input_layer = Input(shape=(420,), name='UserScore')
input_content = Input(shape=(50,), name='Itemcontent')
# Encoder
# -----------------------------
enc = Dense(512, activation=params["activation"], name='EncLayer1')(input_layer)
# Content Information
#embbeding Turns positive integers (indexes) into dense vectors of fixed size.
x_content = Embedding(100, params['firtr_neurons'], input_length=50)(input_content)
x_content = Flatten()(x_content)
x_content = Dense(params['firtr_neurons'], activation=params["activation"],
name='ItemLatentSpace')(x_content)
# Latent Space
# -----------------------------
# Dropout layer randomly sets input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting. Inputs not set to 0 are scaled up by 1/(1 - rate) such that the sum over all inputs is unchanged.
lat_space = Dense(params['firtr_neurons'], activation=params["activation"], name='UserLatentSpace')(enc)
lat_space= add([lat_space, x_content], name='LatentSpace')
lat_space = Dropout(params["dropout"], name='Dropout')(lat_space) # Dropout
# Decoder
# -----------------------------
dec = Dense(params['firtr_neurons']*2, activation=params["activation"], name='DecLayer1')(lat_space)
# Output
output_layer = Dense(420, activation='linear', name='UserScorePred')(dec)
# this model maps an input to its reconstruction
model = Model([input_layer, input_content], output_layer)
#model.compile(optimizer = SGD(lr=0.0001), loss='mse')
model.compile(
optimizer="Adam",
loss='mean_squared_error',)
model.summary()
out = model.fit(x=x_train,
y=y_train,
validation_data=(x_val, y_val),
epochs=50,
batch_size=params['batch_size'],
verbose=0)
return out,model
p = {#'lr': (0.5, 5, 10),
#'hidden_layers':[0, 1, 2],
'batch_size': [50,100,150],
'epochs': [50,60],
#'times':[216,300,600],
'firtr_neurons':[216,316],
'dropout': [ .8,.9],
#'weight_regulizer':[None],
#'emb_output_dims': [None],
#'optimizer': ["Adam", "Nadam", "RMSprop"],
'activation':[ "selu","relu"],
}
scan_object = ta.Scan(x=[x_train, x1_train],
y=y_train,
x_val=[x_val, x1_val],
y_val=y_val,
params=p,
model=autoEncoder,
experiment_name="1",
)
talos.Evaluate(scan_object)
from talos.
Thank you so much :)
from talos.
from talos.
Sorry for not replying earlier. Have you looked at this example for multiple inputs: https://autonomio.github.io/talos/#/Examples_Multiple_Inputs
from talos.
This will be handled in #582 so merging with that.
from talos.
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