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

Question

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

我不知道问题是什么，因为我在创建 numpy 数组时使用 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，表示大量 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，但问题仍然出现!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())

Answer 1

当我使用 keras-bert 进行运行分类时，我也在 google colab tpu 中遇到了这个错误。我调低了批量大小和最大长度，然后错误消失了，我不知道为什么。因此您可以尝试调小模型中的批量大小。