Я создал класс, последовательно добавляющий слои к autoencoder, с целью изучения представления данных из более высоких и более высоких характеристик.
Однако после тренировки моего алгоритма и использования функции предсказания выходная форма не является правильной.
x: shape = (6000, 23)
alg = SuccessiveAutoencoder()
alg.fit(x, epochs = 10)
x_compressed = alg.predict(x)
x_compressed should have a shape (6000,5) but instead has (6000, 23)
Может ли кто-нибудь помочь мне в этом? где проблема?
Пожалуйста, найдите код ниже:
class SuccessiveAutoencoder():
"""The algorithm stacks autoencoders one after others, with the idea to
learn from the data higher level features.
Attributes
-----------------
- hidden_layers: number of hidden layers
- units_nbr: number of neurons in each hidden layer
- activation: type of activation to be used. Note that the activation of
the decoder is hardcoded within the class, and is a sigmoid
- fine_tuning: if the algorithm train trains the full network once this one
has been built. It can be 'y' or 'n'
"""
def __init__(self, hidden_layers = 5, units_nbr = 5, activation = 'relu',
fine_tuning = 'y'):
"""Initialize the algorithm"""
self.hidden_layers = hidden_layers
self.units_nbr = units_nbr
self.activation = activation
self.fine_tuning = fine_tuning
def create_model(self, X):
"""Create the inital autoencoder structure"""
# Build simple autoencoder
input_time_series = Input(shape = (X.shape[1], ), name = 'input')
layer1 = Dense(units = self.units_nbr, activation = self.activation,
name = 'hidden1')(input_time_series)
decoded = Dense(units = X.shape[1], activation = 'sigmoid', name = 'output')(layer1)
return Model(input_time_series, decoded)
def add_layer(self, model, incr):
"""Add a new layer, and make previous layers not trainable"""
# Set previous layers not trainable
for layer in model.layers[:-1]:
layer.trainable = False
# Output of previous layer
out = model.layers[-2].output
# Add the new layer
layer_new = Dense(units = self.units_nbr, activation = self.activation,
name = 'hidden' + str(incr))(out)
decoded = model.layers[-1](layer_new)
return Model(model.layers[0].input, decoded)
def fit(self, X, epochs):
"""Run the algorithm, creating the autoencoder and calibrating it"""
# Create the model and train it
print('/ Training Hidden Layer 1')
model = self.create_model(X)
model.compile(loss = 'mean_squared_error', optimizer = 'adam')
h = model.fit(X, X, epochs = epochs, verbose = 0)
print('Last loss: {}'.format(h.history['loss'][-1]))
# Incrementally add layer, and train these new layers
for incr in range(2, self.hidden_layers + 1):
print('/ Training Hidden Layer {}'.format(str(incr)))
model = self.add_layer(model, incr)
model.compile(loss = 'mean_squared_error', optimizer = 'adam')
h = model.fit(X, X, epochs = epochs, verbose = 0)
print('Last loss: {}'.format(h.history['loss'][-1]))
# If the user wants to run the calibration again over the complete model
if self.fine_tuning == 'y':
# Final training
print('/ Final Tuning')
for layer in model.layers:
layer.trainable = True
model.compile(loss = 'mean_squared_error', optimizer = 'adam')
h = model.fit(X, X, epochs = epochs, verbose = 0)
print('Last loss: {}'.format(h.history['loss'][-1]))
# Get rid of last layer, and stored the model
model.layers.pop()
model.compile(loss = 'mean_squared_error', optimizer = 'adam')
self.model = model
def predict(self, X):
"""Predict the compressed information from X"""
return self.model.predict(X)