python - 有时这种 tensorflow 训练有效，有时却无效

问题描述

我正在学习 Python Tensorflow（机器学习），以下示例以前可以使用，但现在它突然开始失败。

import tensorflow as tf
import numpy as np
from tensorflow import keras

我收到以下警告：

这是我的代码：

EPOCHS = 200
BATCH_SIZE = 128
VERBOSE = 1
NB_CLASSES = 10
N_HIDDEN = 128
VALIDATION_SPLIT = 0.2
DROPOUT = 0.3

## loading MNIST dataset
# Labels have one-hot representation
mnist = keras.datasets.mnist
(X_train, Y_train), (X_test, Y_test) = mnist.load_data()

## X_train is 60000 rows of 28x28 values; we reshape it to 
60000 * 784
RESHAPED = 784
#
X_train = X_train.reshape(60000, RESHAPED)
X_test = X_test.reshape(10000, RESHAPED)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

# Normalise inputs within [0,1]

X_train, X_test = X_train / 255, X_test / 255
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

Y_train = tf.keras.utils.to_categorical(Y_train, 
NB_CLASSES)
Y_test = tf.keras.utils.to_categorical(Y_test, NB_CLASSES)

# One Hot representation for labels

Y_train = tf.keras.utils.to_categorical (Y_train, 
NB_CLASSES)
y_test = tf.keras.utils.to_categorical (Y_test, NB_CLASSES)


# Build the model.
model = tf.keras.models.Sequential()
model.add(keras.layers.Dense (N_HIDDEN, input_shape= 
(RESHAPED,), name='dense_layer', activation='relu'))
model.add(keras.layers.Dropout (DROPOUT))


model.add(keras.layers.Dense (N_HIDDEN, 
name='dense_layer_2', activation='relu'))
model.add(keras.layers.Dropout (DROPOUT))


model.add(keras.layers.Dense (NB_CLASSES, 
name='dense_layer_3', activation='softmax'))


# Compile the model
model.compile(optimizer='SGD',
         loss='categorical_crossentropy',
         metrics=['accuracy'])

# Training the model

model.fit(X_train, Y_train,
     batch_size=BATCH_SIZE,
     epochs=EPOCHS,
     verbose=VERBOSE,
     validation_split=VALIDATION_SPLIT)

这是训练失败的地方：

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-10-51b78dc3a33e> in <module>
      5          epochs=EPOCHS,
      6          verbose=VERBOSE,
----> 7          validation_split=VALIDATION_SPLIT)

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    778           validation_steps=validation_steps,
    779           validation_freq=validation_freq,
--> 780           steps_name='steps_per_epoch')
    781 
    782   def evaluate(self,

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py in model_iteration(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)
    361 
    362         # Get outputs.
--> 363         batch_outs = f(ins_batch)
    364         if not isinstance(batch_outs, list):
    365           batch_outs = [batch_outs]

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\backend.py in __call__(self, inputs)
   3290 
   3291     fetched = self._callable_fn(*array_vals,
-> 3292                                 run_metadata=self.run_metadata)
   3293     self._call_fetch_callbacks(fetched[-len(self._fetches):])
   3294     output_structure = nest.pack_sequence_as(

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args, **kwargs)
   1456         ret = tf_session.TF_SessionRunCallable(self._session._session,
   1457                                                self._handle, args,
-> 1458                                                run_metadata_ptr)
   1459         if run_metadata:
   1460           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

InvalidArgumentError: logits and labels must be broadcastable: logits_size=[128,10] labels_size=[1280,10]
     [[{{node loss_1/dense_layer_3_loss/softmax_cross_entropy_with_logits}}]]

我在这里做错了什么？

标签： pythonpython-3.xtensorflowkeras

Y_train = tf.keras.utils.to_categorical(Y_train, NB_CLASSES) Y_test = tf.keras.utils.to_categorical(Y_test, NB_CLASSES) # One Hot representation for labels Y_train = tf.keras.utils.to_categorical (Y_train, NB_CLASSES) y_test = tf.keras.utils.to_categorical (Y_test, NB_CLASSES)

python - 有时这种 tensorflow 训练有效，有时却无效

问题描述

解决方案

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