machine-learning - Colab TPU错误-由输出cond_8/Merge:0导致的InvalidArgumentError : Unsupported data type for TPU: double,

我正在尝试使用TPU在google colab上进行一些基本的字符分类。我收到以下错误:

InvalidArgumentError: Unsupported data type for TPU: double, caused by output cond_8/Merge:0

我不知道问题出在哪里，因为我在创建numpy数组时使用的是float32。我也不知道cond_8/Merge:0指的是什么。我加载的输入文件是一个JSON数组，表示很多28x28灰度图像

[{"label":25,"data":[[[1],[.56720000]...],...]}]

我尝试注释掉除第一个输入层之外的所有层，但问题仍然存在!!我的代码是:

import os, re, math, json, shutil, pprint
import PIL.Image, PIL.ImageFont, PIL.ImageDraw
import numpy as np
import json
import tensorflow as tf
from matplotlib import pyplot as plt
from tensorflow.python.platform import tf_logging
from google.colab import drive
print("Tensorflow version " + tf.__version__)

with open('/tmp/encoded.json') as json_file:
    data = json.load(json_file)

print("Got data")

images_data = list(map(lambda row: row["data"],data))
label_data = list(map(lambda row: row["label"],data))

print("mapped data")

images_data_tensor = np.asarray(images_data, dtype=np.float32)
label_data_tensor = np.asarray(label_data, dtype=np.float32)

print("converted to tensors")

BATCH_SIZE = 128


N = 24


# This model trains to 99.4% sometimes 99.5% accuracy in 10 epochs (with a batch size of 32)
def create_model():
  l = tf.keras.layers
  model = tf.keras.Sequential(
    [
      #l.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1)),

      l.Conv2D(input_shape=(28,28,1,), filters=6, kernel_size=3, padding='same', use_bias=False), # no bias necessary before batch norm
      l.BatchNormalization(scale=False, center=True), # no batch norm scaling necessary before "relu"
      l.Activation('relu'), # activation after batch norm

      l.Conv2D(filters=12, kernel_size=6, padding='same', use_bias=False, strides=2),
      l.BatchNormalization(scale=False, center=True),
      l.Activation('relu'),

      l.Conv2D(filters=24, kernel_size=6, padding='same', use_bias=False, strides=2),
      l.BatchNormalization(scale=False, center=True),
      l.Activation('relu'),

      l.Flatten(),
      l.Dense(200, use_bias=False),
      l.BatchNormalization(scale=False, center=True),
      l.Activation('relu'),
      l.Dropout(0.5), # Dropout on dense layer only

      l.Dense(10, activation='softmax')
    ])
  return model

# set up learning rate decay
lr_decay = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 0.0001 + 0.02 * math.pow(0.5, 1+epoch), verbose=True)


EPOCHS = 10
tpu = None

# Default strategy for GPU/CPU. Note that tensorflow-gpu will need to be installed for GPU to work
strategy = tf.distribute.MirroredStrategy()

try: # TPU detection
  tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # Picks up a connected TPU on Google's Colab, ML Engine, Kubernetes and Deep Learning VMs accessed through the 'ctpu up' utility
  #tpu = tf.distribute.cluster_resolver.TPUClusterResolver('MY_TPU_NAME') # If auto-detection does not work, you can pass the name of the TPU explicitly (tip: on a VM created with "ctpu up" the TPU has the same name as the VM)
  tf.tpu.experimental.initialize_tpu_system(tpu)
  strategy = tf.distribute.experimental.TPUStrategy(tpu)
except ValueError:
  print('Training on CPU')

with strategy.scope():
  trained_model = create_model()
  trained_model.compile(optimizer='adam', # learning rate will be set by LearningRateScheduler
                loss='categorical_crossentropy',
                metrics=['accuracy'])

  # print model layers
  trained_model.summary()

  history = trained_model.fit(x=images_data_tensor,y=label_data_tensor, epochs=EPOCHS, callbacks=[lr_decay])


print(history.history.keys())

最佳答案

当我使用keras-bert进行运行分类时，我也在google colab tpu中遇到此错误。
我调低批大小和最大长度，然后错误消失了，我不知道为什么。因此，您可以尝试减小模型中的批次大小。

关于machine-learning - Colab TPU错误-由输出cond_8/Merge:0导致的InvalidArgumentError : Unsupported data type for TPU: double,，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/57224105/