ktrain/lroptimize/lrfinder.py
def plot_loss(self, n_skip_beginning=10, n_skip_end=1):
        """
        Plots the loss.
        Parameters:
            n_skip_beginning - number of batches to skip on the left.
            n_skip_end - number of batches to skip on the right.
            highlight - will highlight numerical estimate
                        of best lr if True
        """
        fig, ax = plt.subplots()
        plt.ylabel("loss")
        plt.xlabel("learning rate (log scale)")
        ax.plot(self.lrs[n_skip_beginning:-n_skip_end], self.losses[n_skip_beginning:-n_skip_end])
        plt.xscale('log')
        plt.show()
        return

        Plots the loss.
        Parameters:
            n_skip_beginning - number of batches to skip on the left.
            n_skip_end - number of batches to skip on the right.
            highlight - will highlight numerical estimate
                        of best lr if True
        
Plots the loss.
Parameters:
n_skip_beginning - number of batches to skip on the left.
n_skip_end - number of batches to skip on the right.
highlight - will highlight numerical estimate
of best lr if True
Plots the loss . Parameters : n_skip_beginning - number of batches to skip on the left . n_skip_end - number of batches to skip on the right . highlight - will highlight numerical estimate of best lr if True
def plot_loss(self, n_skip_beginning=10, n_skip_end=1):
        fig, ax = plt.subplots()
        plt.ylabel("loss")
        plt.xlabel("learning rate (log scale)")
        ax.plot(self.lrs[n_skip_beginning:-n_skip_end], self.losses[n_skip_beginning:-n_skip_end])
        plt.xscale('log')
        plt.show()
        return
def plot_loss ( self , n_skip_beginning = 10 , n_skip_end = 1 ) : fig , ax = plt . subplots ( ) plt . ylabel ( "loss" ) plt . xlabel ( "learning rate (log scale)" ) ax . plot ( self . lrs [ n_skip_beginning : - n_skip_end ] , self . losses [ n_skip_beginning : - n_skip_end ] ) plt . xscale ( 'log' ) plt . show ( ) return
Plots the loss.
Plots the loss .
"""
        Plots the loss.
        Parameters:
            n_skip_beginning - number of batches to skip on the left.
            n_skip_end - number of batches to skip on the right.
            highlight - will highlight numerical estimate
                        of best lr if True
        """
self
n_skip_beginning
n_skip_end