सहायता Kaggle पर TensorFlow साथ ग्रेट बैरियर रीफ की रक्षा चैलेंज में शामिल हों

अपनी खुद की कॉलबैक लिखना

TensorFlow.org पर देखें Google Colab में चलाएं GitHub पर स्रोत देखें नोटबुक डाउनलोड करें

परिचय

प्रशिक्षण, मूल्यांकन या अनुमान के दौरान केरस मॉडल के व्यवहार को अनुकूलित करने के लिए कॉलबैक एक शक्तिशाली उपकरण है। उदाहरणों में शामिल हैं tf.keras.callbacks.TensorBoard प्रशिक्षण प्रगति और TensorBoard साथ परिणाम, या कल्पना करने के लिए tf.keras.callbacks.ModelCheckpoint प्रशिक्षण के दौरान अपने मॉडल को बचाने के लिए समय समय के लिए।

इस गाइड में, आप सीखेंगे कि केरस कॉलबैक क्या है, यह क्या कर सकता है, और आप अपना खुद का निर्माण कैसे कर सकते हैं। हम आपको आरंभ करने के लिए सरल कॉलबैक एप्लिकेशन के कुछ डेमो प्रदान करते हैं।

सेट अप

import tensorflow as tf
from tensorflow import keras

केरस कॉलबैक अवलोकन

सभी कॉलबैक उपवर्ग keras.callbacks.Callback वर्ग, और विधियों प्रशिक्षण के विभिन्न चरणों, परीक्षण, और भविष्यवाणी पर कहा जाता है का एक सेट को ओवरराइड। प्रशिक्षण के दौरान आंतरिक स्थिति और मॉडल के आँकड़ों को देखने के लिए कॉलबैक उपयोगी होते हैं।

तुम एक (कीवर्ड तर्क के रूप में कॉलबैक की सूची पारित कर सकते हैं callbacks ) निम्नलिखित मॉडल के तरीकों के लिए:

कॉलबैक विधियों का अवलोकन

वैश्विक तरीके

on_(train|test|predict)_begin(self, logs=None)

की शुरुआत में कहा जाता है fit / evaluate / predict

on_(train|test|predict)_end(self, logs=None)

के अंत में कहा जाता है fit / evaluate / predict

प्रशिक्षण/परीक्षण/पूर्वानुमान के लिए बैच-स्तरीय तरीके

on_(train|test|predict)_batch_begin(self, batch, logs=None)

प्रशिक्षण/परीक्षण/भविष्यवाणी के दौरान बैच को संसाधित करने से ठीक पहले कॉल किया जाता है।

on_(train|test|predict)_batch_end(self, batch, logs=None)

प्रशिक्षण/परीक्षण/बैच की भविष्यवाणी के अंत में बुलाया गया। इस विधि के भीतर, logs एक dict मैट्रिक्स परिणाम युक्त है।

युग-स्तर के तरीके (केवल प्रशिक्षण)

on_epoch_begin(self, epoch, logs=None)

प्रशिक्षण के दौरान एक युग की शुरुआत में बुलाया गया।

on_epoch_end(self, epoch, logs=None)

प्रशिक्षण के दौरान एक युग के अंत में बुलाया गया।

एक बुनियादी उदाहरण

आइए एक ठोस उदाहरण देखें। आरंभ करने के लिए, आइए टेंसरफ़्लो आयात करें और एक साधारण अनुक्रमिक केरस मॉडल को परिभाषित करें:

# Define the Keras model to add callbacks to
def get_model():
    model = keras.Sequential()
    model.add(keras.layers.Dense(1, input_dim=784))
    model.compile(
        optimizer=keras.optimizers.RMSprop(learning_rate=0.1),
        loss="mean_squared_error",
        metrics=["mean_absolute_error"],
    )
    return model

फिर, केरस डेटासेट एपीआई से प्रशिक्षण और परीक्षण के लिए एमएनआईएसटी डेटा लोड करें:

# Load example MNIST data and pre-process it
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(-1, 784).astype("float32") / 255.0
x_test = x_test.reshape(-1, 784).astype("float32") / 255.0

# Limit the data to 1000 samples
x_train = x_train[:1000]
y_train = y_train[:1000]
x_test = x_test[:1000]
y_test = y_test[:1000]

अब, एक साधारण कस्टम कॉलबैक परिभाषित करें जो लॉग करता है:

  • जब fit / evaluate / predict शुरू होता है और समाप्त होता है
  • जब हर युग शुरू और समाप्त होता है
  • जब प्रत्येक प्रशिक्षण बैच प्रारंभ और समाप्त होता है
  • जब प्रत्येक मूल्यांकन (परीक्षण) बैच प्रारंभ और समाप्त होता है
  • जब प्रत्येक अनुमान (भविष्यवाणी) बैच प्रारंभ और समाप्त होता है
class CustomCallback(keras.callbacks.Callback):
    def on_train_begin(self, logs=None):
        keys = list(logs.keys())
        print("Starting training; got log keys: {}".format(keys))

    def on_train_end(self, logs=None):
        keys = list(logs.keys())
        print("Stop training; got log keys: {}".format(keys))

    def on_epoch_begin(self, epoch, logs=None):
        keys = list(logs.keys())
        print("Start epoch {} of training; got log keys: {}".format(epoch, keys))

    def on_epoch_end(self, epoch, logs=None):
        keys = list(logs.keys())
        print("End epoch {} of training; got log keys: {}".format(epoch, keys))

    def on_test_begin(self, logs=None):
        keys = list(logs.keys())
        print("Start testing; got log keys: {}".format(keys))

    def on_test_end(self, logs=None):
        keys = list(logs.keys())
        print("Stop testing; got log keys: {}".format(keys))

    def on_predict_begin(self, logs=None):
        keys = list(logs.keys())
        print("Start predicting; got log keys: {}".format(keys))

    def on_predict_end(self, logs=None):
        keys = list(logs.keys())
        print("Stop predicting; got log keys: {}".format(keys))

    def on_train_batch_begin(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Training: start of batch {}; got log keys: {}".format(batch, keys))

    def on_train_batch_end(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Training: end of batch {}; got log keys: {}".format(batch, keys))

    def on_test_batch_begin(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Evaluating: start of batch {}; got log keys: {}".format(batch, keys))

    def on_test_batch_end(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Evaluating: end of batch {}; got log keys: {}".format(batch, keys))

    def on_predict_batch_begin(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Predicting: start of batch {}; got log keys: {}".format(batch, keys))

    def on_predict_batch_end(self, batch, logs=None):
        keys = list(logs.keys())
        print("...Predicting: end of batch {}; got log keys: {}".format(batch, keys))

आइए इसे आजमाएं:

model = get_model()
model.fit(
    x_train,
    y_train,
    batch_size=128,
    epochs=1,
    verbose=0,
    validation_split=0.5,
    callbacks=[CustomCallback()],
)

res = model.evaluate(
    x_test, y_test, batch_size=128, verbose=0, callbacks=[CustomCallback()]
)

res = model.predict(x_test, batch_size=128, callbacks=[CustomCallback()])
Starting training; got log keys: []
Start epoch 0 of training; got log keys: []
...Training: start of batch 0; got log keys: []
...Training: end of batch 0; got log keys: ['loss', 'mean_absolute_error']
...Training: start of batch 1; got log keys: []
...Training: end of batch 1; got log keys: ['loss', 'mean_absolute_error']
...Training: start of batch 2; got log keys: []
...Training: end of batch 2; got log keys: ['loss', 'mean_absolute_error']
...Training: start of batch 3; got log keys: []
...Training: end of batch 3; got log keys: ['loss', 'mean_absolute_error']
Start testing; got log keys: []
...Evaluating: start of batch 0; got log keys: []
...Evaluating: end of batch 0; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 1; got log keys: []
...Evaluating: end of batch 1; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 2; got log keys: []
...Evaluating: end of batch 2; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 3; got log keys: []
...Evaluating: end of batch 3; got log keys: ['loss', 'mean_absolute_error']
Stop testing; got log keys: ['loss', 'mean_absolute_error']
End epoch 0 of training; got log keys: ['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error']
Stop training; got log keys: ['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error']
Start testing; got log keys: []
...Evaluating: start of batch 0; got log keys: []
...Evaluating: end of batch 0; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 1; got log keys: []
...Evaluating: end of batch 1; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 2; got log keys: []
...Evaluating: end of batch 2; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 3; got log keys: []
...Evaluating: end of batch 3; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 4; got log keys: []
...Evaluating: end of batch 4; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 5; got log keys: []
...Evaluating: end of batch 5; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 6; got log keys: []
...Evaluating: end of batch 6; got log keys: ['loss', 'mean_absolute_error']
...Evaluating: start of batch 7; got log keys: []
...Evaluating: end of batch 7; got log keys: ['loss', 'mean_absolute_error']
Stop testing; got log keys: ['loss', 'mean_absolute_error']
Start predicting; got log keys: []
...Predicting: start of batch 0; got log keys: []
...Predicting: end of batch 0; got log keys: ['outputs']
...Predicting: start of batch 1; got log keys: []
...Predicting: end of batch 1; got log keys: ['outputs']
...Predicting: start of batch 2; got log keys: []
...Predicting: end of batch 2; got log keys: ['outputs']
...Predicting: start of batch 3; got log keys: []
...Predicting: end of batch 3; got log keys: ['outputs']
...Predicting: start of batch 4; got log keys: []
...Predicting: end of batch 4; got log keys: ['outputs']
...Predicting: start of batch 5; got log keys: []
...Predicting: end of batch 5; got log keys: ['outputs']
...Predicting: start of batch 6; got log keys: []
...Predicting: end of batch 6; got log keys: ['outputs']
...Predicting: start of batch 7; got log keys: []
...Predicting: end of batch 7; got log keys: ['outputs']
Stop predicting; got log keys: []

का उपयोग logs dict

logs dict नुकसान मूल्य, और एक बैच या युग के अंत में सभी मीट्रिक हैं। उदाहरण में हानि और माध्य निरपेक्ष त्रुटि शामिल है।

class LossAndErrorPrintingCallback(keras.callbacks.Callback):
    def on_train_batch_end(self, batch, logs=None):
        print(
            "Up to batch {}, the average loss is {:7.2f}.".format(batch, logs["loss"])
        )

    def on_test_batch_end(self, batch, logs=None):
        print(
            "Up to batch {}, the average loss is {:7.2f}.".format(batch, logs["loss"])
        )

    def on_epoch_end(self, epoch, logs=None):
        print(
            "The average loss for epoch {} is {:7.2f} "
            "and mean absolute error is {:7.2f}.".format(
                epoch, logs["loss"], logs["mean_absolute_error"]
            )
        )


model = get_model()
model.fit(
    x_train,
    y_train,
    batch_size=128,
    epochs=2,
    verbose=0,
    callbacks=[LossAndErrorPrintingCallback()],
)

res = model.evaluate(
    x_test,
    y_test,
    batch_size=128,
    verbose=0,
    callbacks=[LossAndErrorPrintingCallback()],
)
Up to batch 0, the average loss is   30.79.
Up to batch 1, the average loss is  459.11.
Up to batch 2, the average loss is  314.68.
Up to batch 3, the average loss is  237.97.
Up to batch 4, the average loss is  191.76.
Up to batch 5, the average loss is  160.95.
Up to batch 6, the average loss is  138.74.
Up to batch 7, the average loss is  124.85.
The average loss for epoch 0 is  124.85 and mean absolute error is    6.00.
Up to batch 0, the average loss is    5.13.
Up to batch 1, the average loss is    4.66.
Up to batch 2, the average loss is    4.71.
Up to batch 3, the average loss is    4.66.
Up to batch 4, the average loss is    4.69.
Up to batch 5, the average loss is    4.56.
Up to batch 6, the average loss is    4.77.
Up to batch 7, the average loss is    4.77.
The average loss for epoch 1 is    4.77 and mean absolute error is    1.75.
Up to batch 0, the average loss is    5.73.
Up to batch 1, the average loss is    5.04.
Up to batch 2, the average loss is    5.10.
Up to batch 3, the average loss is    5.14.
Up to batch 4, the average loss is    5.37.
Up to batch 5, the average loss is    5.24.
Up to batch 6, the average loss is    5.22.
Up to batch 7, the average loss is    5.16.

का उपयोग self.model विशेषता

: लॉग जानकारी प्राप्त जब उनके तरीकों में से एक कहा जाता है के अलावा, कॉलबैक प्रशिक्षण / मूल्यांकन / अनुमान के मौजूदा दौर के साथ जुड़े मॉडल की पहुंच है self.model

यहाँ बातें आप के साथ क्या कर सकते हैं कुछ के हैं self.model एक कॉलबैक में:

  • सेट self.model.stop_training = True तुरंत बाधा प्रशिक्षण के लिए।
  • अनुकूलक का उत्परिवर्तन hyperparameters (के रूप में उपलब्ध self.model.optimizer इस तरह के रूप में), self.model.optimizer.learning_rate
  • मॉडल को समय-समय पर सहेजें।
  • के उत्पादन में रिकॉर्ड model.predict() प्रत्येक युग के अंत में कुछ परीक्षण नमूनों पर, प्रशिक्षण के दौरान एक मानसिक स्वास्थ्य की जांच के रूप में उपयोग करने के लिए।
  • मॉडल समय के साथ क्या सीख रहा है, इसकी निगरानी के लिए प्रत्येक युग के अंत में मध्यवर्ती सुविधाओं के विज़ुअलाइज़ेशन निकालें।
  • आदि।

आइए इसे कुछ उदाहरणों में क्रिया में देखें।

केरस कॉलबैक अनुप्रयोगों के उदाहरण

न्यूनतम नुकसान पर जल्दी रुकना

यह पहला उदाहरण से पता चलता है एक के निर्माण Callback कि प्रशिक्षण बंद हो जाता है जब नुकसान की न्यूनतम विशेषता की स्थापना करके, पर पहुँच गया है self.model.stop_training (बुलियन)। वैकल्पिक रूप से, आप एक तर्क प्रदान कर सकते हैं patience हम एक स्थानीय न्यूनतम पर पहुंच गया करने के बाद रुकने से पहले कितने अवधियों को इंतजार करना चाहिए निर्दिष्ट करने के लिए।

tf.keras.callbacks.EarlyStopping अधिक पूर्ण और सामान्य कार्यान्वयन प्रदान करता है।

import numpy as np


class EarlyStoppingAtMinLoss(keras.callbacks.Callback):
    """Stop training when the loss is at its min, i.e. the loss stops decreasing.

  Arguments:
      patience: Number of epochs to wait after min has been hit. After this
      number of no improvement, training stops.
  """

    def __init__(self, patience=0):
        super(EarlyStoppingAtMinLoss, self).__init__()
        self.patience = patience
        # best_weights to store the weights at which the minimum loss occurs.
        self.best_weights = None

    def on_train_begin(self, logs=None):
        # The number of epoch it has waited when loss is no longer minimum.
        self.wait = 0
        # The epoch the training stops at.
        self.stopped_epoch = 0
        # Initialize the best as infinity.
        self.best = np.Inf

    def on_epoch_end(self, epoch, logs=None):
        current = logs.get("loss")
        if np.less(current, self.best):
            self.best = current
            self.wait = 0
            # Record the best weights if current results is better (less).
            self.best_weights = self.model.get_weights()
        else:
            self.wait += 1
            if self.wait >= self.patience:
                self.stopped_epoch = epoch
                self.model.stop_training = True
                print("Restoring model weights from the end of the best epoch.")
                self.model.set_weights(self.best_weights)

    def on_train_end(self, logs=None):
        if self.stopped_epoch > 0:
            print("Epoch %05d: early stopping" % (self.stopped_epoch + 1))


model = get_model()
model.fit(
    x_train,
    y_train,
    batch_size=64,
    steps_per_epoch=5,
    epochs=30,
    verbose=0,
    callbacks=[LossAndErrorPrintingCallback(), EarlyStoppingAtMinLoss()],
)
Up to batch 0, the average loss is   34.62.
Up to batch 1, the average loss is  405.62.
Up to batch 2, the average loss is  282.27.
Up to batch 3, the average loss is  215.95.
Up to batch 4, the average loss is  175.32.
The average loss for epoch 0 is  175.32 and mean absolute error is    8.59.
Up to batch 0, the average loss is    8.86.
Up to batch 1, the average loss is    7.31.
Up to batch 2, the average loss is    6.51.
Up to batch 3, the average loss is    6.71.
Up to batch 4, the average loss is    6.24.
The average loss for epoch 1 is    6.24 and mean absolute error is    2.06.
Up to batch 0, the average loss is    4.83.
Up to batch 1, the average loss is    5.05.
Up to batch 2, the average loss is    4.71.
Up to batch 3, the average loss is    4.41.
Up to batch 4, the average loss is    4.48.
The average loss for epoch 2 is    4.48 and mean absolute error is    1.68.
Up to batch 0, the average loss is    5.84.
Up to batch 1, the average loss is    5.73.
Up to batch 2, the average loss is    7.24.
Up to batch 3, the average loss is   10.34.
Up to batch 4, the average loss is   15.53.
The average loss for epoch 3 is   15.53 and mean absolute error is    3.20.
Restoring model weights from the end of the best epoch.
Epoch 00004: early stopping
<keras.callbacks.History at 0x7fd0843bf510>

सीखने की दर निर्धारण

इस उदाहरण में, हम दिखाते हैं कि प्रशिक्षण के दौरान ऑप्टिमाइज़र की सीखने की दर को गतिशील रूप से बदलने के लिए कस्टम कॉलबैक का उपयोग कैसे किया जा सकता है।

देखें callbacks.LearningRateScheduler एक अधिक सामान्य कार्यान्वयन के लिए।

class CustomLearningRateScheduler(keras.callbacks.Callback):
    """Learning rate scheduler which sets the learning rate according to schedule.

  Arguments:
      schedule: a function that takes an epoch index
          (integer, indexed from 0) and current learning rate
          as inputs and returns a new learning rate as output (float).
  """

    def __init__(self, schedule):
        super(CustomLearningRateScheduler, self).__init__()
        self.schedule = schedule

    def on_epoch_begin(self, epoch, logs=None):
        if not hasattr(self.model.optimizer, "lr"):
            raise ValueError('Optimizer must have a "lr" attribute.')
        # Get the current learning rate from model's optimizer.
        lr = float(tf.keras.backend.get_value(self.model.optimizer.learning_rate))
        # Call schedule function to get the scheduled learning rate.
        scheduled_lr = self.schedule(epoch, lr)
        # Set the value back to the optimizer before this epoch starts
        tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_lr)
        print("\nEpoch %05d: Learning rate is %6.4f." % (epoch, scheduled_lr))


LR_SCHEDULE = [
    # (epoch to start, learning rate) tuples
    (3, 0.05),
    (6, 0.01),
    (9, 0.005),
    (12, 0.001),
]


def lr_schedule(epoch, lr):
    """Helper function to retrieve the scheduled learning rate based on epoch."""
    if epoch < LR_SCHEDULE[0][0] or epoch > LR_SCHEDULE[-1][0]:
        return lr
    for i in range(len(LR_SCHEDULE)):
        if epoch == LR_SCHEDULE[i][0]:
            return LR_SCHEDULE[i][1]
    return lr


model = get_model()
model.fit(
    x_train,
    y_train,
    batch_size=64,
    steps_per_epoch=5,
    epochs=15,
    verbose=0,
    callbacks=[
        LossAndErrorPrintingCallback(),
        CustomLearningRateScheduler(lr_schedule),
    ],
)
Epoch 00000: Learning rate is 0.1000.
Up to batch 0, the average loss is   26.55.
Up to batch 1, the average loss is  435.15.
Up to batch 2, the average loss is  298.00.
Up to batch 3, the average loss is  225.91.
Up to batch 4, the average loss is  182.66.
The average loss for epoch 0 is  182.66 and mean absolute error is    8.16.

Epoch 00001: Learning rate is 0.1000.
Up to batch 0, the average loss is    7.30.
Up to batch 1, the average loss is    6.22.
Up to batch 2, the average loss is    6.76.
Up to batch 3, the average loss is    6.37.
Up to batch 4, the average loss is    5.98.
The average loss for epoch 1 is    5.98 and mean absolute error is    2.01.

Epoch 00002: Learning rate is 0.1000.
Up to batch 0, the average loss is    4.23.
Up to batch 1, the average loss is    4.56.
Up to batch 2, the average loss is    4.81.
Up to batch 3, the average loss is    4.63.
Up to batch 4, the average loss is    4.67.
The average loss for epoch 2 is    4.67 and mean absolute error is    1.73.

Epoch 00003: Learning rate is 0.0500.
Up to batch 0, the average loss is    6.24.
Up to batch 1, the average loss is    5.62.
Up to batch 2, the average loss is    5.48.
Up to batch 3, the average loss is    5.09.
Up to batch 4, the average loss is    4.68.
The average loss for epoch 3 is    4.68 and mean absolute error is    1.77.

Epoch 00004: Learning rate is 0.0500.
Up to batch 0, the average loss is    3.38.
Up to batch 1, the average loss is    3.83.
Up to batch 2, the average loss is    3.53.
Up to batch 3, the average loss is    3.64.
Up to batch 4, the average loss is    3.76.
The average loss for epoch 4 is    3.76 and mean absolute error is    1.54.

Epoch 00005: Learning rate is 0.0500.
Up to batch 0, the average loss is    3.62.
Up to batch 1, the average loss is    3.79.
Up to batch 2, the average loss is    3.75.
Up to batch 3, the average loss is    3.83.
Up to batch 4, the average loss is    4.37.
The average loss for epoch 5 is    4.37 and mean absolute error is    1.65.

Epoch 00006: Learning rate is 0.0100.
Up to batch 0, the average loss is    6.73.
Up to batch 1, the average loss is    6.13.
Up to batch 2, the average loss is    5.11.
Up to batch 3, the average loss is    4.57.
Up to batch 4, the average loss is    4.21.
The average loss for epoch 6 is    4.21 and mean absolute error is    1.61.

Epoch 00007: Learning rate is 0.0100.
Up to batch 0, the average loss is    3.37.
Up to batch 1, the average loss is    3.83.
Up to batch 2, the average loss is    3.80.
Up to batch 3, the average loss is    3.50.
Up to batch 4, the average loss is    3.31.
The average loss for epoch 7 is    3.31 and mean absolute error is    1.42.

Epoch 00008: Learning rate is 0.0100.
Up to batch 0, the average loss is    5.33.
Up to batch 1, the average loss is    4.84.
Up to batch 2, the average loss is    4.02.
Up to batch 3, the average loss is    3.87.
Up to batch 4, the average loss is    3.85.
The average loss for epoch 8 is    3.85 and mean absolute error is    1.53.

Epoch 00009: Learning rate is 0.0050.
Up to batch 0, the average loss is    1.84.
Up to batch 1, the average loss is    2.75.
Up to batch 2, the average loss is    3.16.
Up to batch 3, the average loss is    3.52.
Up to batch 4, the average loss is    3.34.
The average loss for epoch 9 is    3.34 and mean absolute error is    1.43.

Epoch 00010: Learning rate is 0.0050.
Up to batch 0, the average loss is    2.36.
Up to batch 1, the average loss is    2.91.
Up to batch 2, the average loss is    2.63.
Up to batch 3, the average loss is    2.93.
Up to batch 4, the average loss is    3.17.
The average loss for epoch 10 is    3.17 and mean absolute error is    1.36.

Epoch 00011: Learning rate is 0.0050.
Up to batch 0, the average loss is    3.32.
Up to batch 1, the average loss is    3.02.
Up to batch 2, the average loss is    2.96.
Up to batch 3, the average loss is    2.80.
Up to batch 4, the average loss is    2.92.
The average loss for epoch 11 is    2.92 and mean absolute error is    1.32.

Epoch 00012: Learning rate is 0.0010.
Up to batch 0, the average loss is    4.11.
Up to batch 1, the average loss is    3.70.
Up to batch 2, the average loss is    3.89.
Up to batch 3, the average loss is    3.76.
Up to batch 4, the average loss is    3.45.
The average loss for epoch 12 is    3.45 and mean absolute error is    1.44.

Epoch 00013: Learning rate is 0.0010.
Up to batch 0, the average loss is    3.38.
Up to batch 1, the average loss is    3.34.
Up to batch 2, the average loss is    3.26.
Up to batch 3, the average loss is    3.56.
Up to batch 4, the average loss is    3.62.
The average loss for epoch 13 is    3.62 and mean absolute error is    1.44.

Epoch 00014: Learning rate is 0.0010.
Up to batch 0, the average loss is    2.48.
Up to batch 1, the average loss is    2.38.
Up to batch 2, the average loss is    2.76.
Up to batch 3, the average loss is    2.63.
Up to batch 4, the average loss is    2.66.
The average loss for epoch 14 is    2.66 and mean absolute error is    1.29.
<keras.callbacks.History at 0x7fd08446c290>

बिल्ट-इन केरस कॉलबैक

पढ़कर मौजूदा Keras कॉलबैक की जाँच के लिए सुनिश्चित करें एपीआई डॉक्स । अनुप्रयोगों में CSV में लॉग इन करना, मॉडल को सहेजना, TensorBoard में मेट्रिक्स की कल्पना करना और बहुत कुछ शामिल है!