
# import the necessary packages
from keras.callbacks import LambdaCallback
from keras import backend as K
import matplotlib.pyplot as plt
import numpy as np
import tempfile


class LearningRateFinder:
    def __init__(self, model, stopFactor=4, beta=0.98):
        # store the model, stop factor, and beta value (for computing
        # a smoothed, average loss)
        self.model = model
        self.stopFactor = stopFactor
        self.beta = beta

        # initialize our list of learning rates and losses,
        # respectively
        self.lrs = []
        self.losses = []

        # initialize our learning rate multiplier, average loss, best
        # loss found thus far, current batch number, and weights file
        self.lrMult = 1
        self.avgLoss = 0
        self.bestLoss = 1e9
        self.batchNum = 0
        self.weightsFile = None

    def reset(self):
        # re-initialize all variables from our constructor
        self.lrs = []
        self.losses = []
        self.lrMult = 1
        self.avgLoss = 0
        self.bestLoss = 1e9
        self.batchNum = 0
        self.weightsFile = None

    def is_data_iter(self, data):
        # define the set of class types we will check for
        iterClasses = ["NumpyArrayIterator", "DirectoryIterator",
             "Iterator", "Sequence"]

        # return whether our data is an iterator
        return data.__class__.__name__ in iterClasses

    def on_batch_end(self, batch, logs):
        # grab the current learning rate and add log it to the list of
        # learning rates that we've tried
        lr = K.get_value(self.model.optimizer.lr)
        self.lrs.append(lr)

        # grab the loss at the end of this batch, increment the total
        # number of batches processed, compute the average average
        # loss, smooth it, and update the losses list with the
        # smoothed value
        l = logs["loss"]
        self.batchNum += 1
        self.avgLoss = (self.beta * self.avgLoss) + ((1 - self.beta) * l)
        smooth = self.avgLoss / (1 - (self.beta ** self.batchNum))
        self.losses.append(smooth)

        # compute the maximum loss stopping factor value
        stopLoss = self.stopFactor * self.bestLoss

        # check to see whether the loss has grown too large
        if self.batchNum > 1 and smooth > stopLoss:
            # stop returning and return from the method
            self.model.stop_training = True
            return

        # check to see if the best loss should be updated
        if self.batchNum == 1 or smooth < self.bestLoss:
            self.bestLoss = smooth

        # increase the learning rate
        lr *= self.lrMult
        K.set_value(self.model.optimizer.lr, lr)

    def find(self, trainData, startLR, endLR, epochs=None,
        stepsPerEpoch=None, batchSize=32, sampleSize=2048,
        verbose=1):
        # reset our class-specific variables
        self.reset()

        # determine if we are using a data generator or not
        useGen = self.is_data_iter(trainData)

        # if we're using a generator and the steps per epoch is not
        # supplied, raise an error
        if useGen and stepsPerEpoch is None:
            msg = "Using generator without supplying stepsPerEpoch"
            raise Exception(msg)

        # if we're not using a generator then our entire dataset must
        # already be in memory
#        elif not useGen:
#            # grab the number of samples in the training data and
#            # then derive the number of steps per epoch
#            numSamples = len(trainData[0])
#            stepsPerEpoch = np.ceil(numSamples / float(batchSize))

        # if no number of training epochs are supplied, compute the
        # training epochs based on a default sample size
        if epochs is None:
            epochs = int(np.ceil(sampleSize / float(stepsPerEpoch)))

        # compute the total number of batch updates that will take
        # place while we are attempting to find a good starting
        # learning rate
        numBatchUpdates = epochs * stepsPerEpoch

        # derive the learning rate multiplier based on the ending
        # learning rate, starting learning rate, and total number of
        # batch updates
        self.lrMult = (endLR / startLR) ** (1.0 / numBatchUpdates)

        # create a temporary file path for the model weights and
        # then save the weights (so we can reset the weights when we
        # are done)
        self.weightsFile = tempfile.mkstemp()[1]
        self.model.save_weights(self.weightsFile)

        # grab the *original* learning rate (so we can reset it
        # later), and then set the *starting* learning rate
        origLR = K.get_value(self.model.optimizer.lr)
        K.set_value(self.model.optimizer.lr, startLR)

        # construct a callback that will be called at the end of each
        # batch, enabling us to increase our learning rate as training
        # progresses
        callback = LambdaCallback(on_batch_end=lambda batch, logs:
            self.on_batch_end(batch, logs))

        self.model.fit_generator(
            trainData,
            steps_per_epoch=stepsPerEpoch,
            epochs=epochs,
            verbose=verbose,
            callbacks=[callback])
        '''
        # check to see if we are using a data iterator
        if useGen:
            self.model.fit_generator(
                trainData,
                steps_per_epoch=stepsPerEpoch,
                epochs=epochs,
                verbose=verbose,
                callbacks=[callback])

        # otherwise, our entire training data is already in memory
        else:
            # train our model using Keras' fit method
            self.model.fit(
                trainData[0], trainData[1],
                batch_size=batchSize,
                epochs=epochs,
                callbacks=[callback],
                verbose=verbose)
        '''
        # restore the original model weights and learning rate
        self.model.load_weights(self.weightsFile)
        K.set_value(self.model.optimizer.lr, origLR)

    def plot_loss(self, skipBegin=10, skipEnd=1, title=""):
        # grab the learning rate and losses values to plot
        lrs = self.lrs[skipBegin:-skipEnd]
        losses = self.losses[skipBegin:-skipEnd]

        # plot the learning rate vs. loss
        plt.plot(lrs, losses)
        plt.xscale("log")
        plt.xlabel("Learning Rate (Log Scale)")
        plt.ylabel("Loss")

        # if the title is not empty, add it to the plot
        if title != "":
            plt.title(title)
