def on_train_begin(self, logs={}):
Callback.on_train_begin(self, logs=logs)
# initialize list of losses
self.losses = []
self.val_losses = []
# initialize plotting
plt.ion()
fig = plt.figure()
self.ax = fig.add_subplot(111)
def on_train_begin(self, logs={}):
Callback.on_train_begin(self, logs=logs)
fig = plt.figure()
self.axFig = fig.add_subplot(111)
# DO PCA
self.pca = decomposition.PCA(n_components=2)
self.pca.fit(self.xTarget)
self.targetEmbedding = np.dot(self.xTarget,
self.pca.components_[[0, 1]].transpose())
