Keras - 具有经常丢失的 GRU 层 - 损失：'nan'，准确度：0

Posted 2023-02-16

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【中文标题】Keras - 具有经常丢失的 GRU 层 - 损失：\'nan\'，准确度：0【英文标题】：Keras - GRU layer with recurrent dropout - loss: 'nan', accuracy: 0Keras - 具有经常丢失的 GRU 层 - 损失：'nan'，准确度：0 【发布时间】：2020-07-02 23:33:25 【问题描述】：

问题描述

我正在阅读 François Chollet (publisher webpage, notebooks on github) 的“Python 中的深度学习”。复制第 6 章中的示例，我遇到了（我相信）GRU 层经常丢失的问题。

我第一次观察到这些错误的代码很长，所以我决定坚持最简单的问题，它可以复制错误：将 IMDB 评论分为“正面”和“负面”类别。

当我使用具有经常性 dropout 训练损失的 GRU 层时（在第一个 epoch 的几批之后）取“值”nan，而训练准确度（从第二个 epoch 开始）取值为 0。

   64/12000 [..............................] - ETA: 3:05 - loss: 0.6930 - accuracy: 0.4844
  128/12000 [..............................] - ETA: 2:09 - loss: 0.6926 - accuracy: 0.4766
  192/12000 [..............................] - ETA: 1:50 - loss: 0.6910 - accuracy: 0.5573
(...) 
 3136/12000 [======>.......................] - ETA: 59s - loss: 0.6870 - accuracy: 0.5635
 3200/12000 [=======>......................] - ETA: 58s - loss: 0.6862 - accuracy: 0.5650
 3264/12000 [=======>......................] - ETA: 58s - loss: 0.6860 - accuracy: 0.5650
 3328/12000 [=======>......................] - ETA: 57s - loss: nan - accuracy: 0.5667   
 3392/12000 [=======>......................] - ETA: 57s - loss: nan - accuracy: 0.5560
 3456/12000 [=======>......................] - ETA: 56s - loss: nan - accuracy: 0.5457
(...)
11840/12000 [============================>.] - ETA: 1s - loss: nan - accuracy: 0.1593
11904/12000 [============================>.] - ETA: 0s - loss: nan - accuracy: 0.1584
11968/12000 [============================>.] - ETA: 0s - loss: nan - accuracy: 0.1576
12000/12000 [==============================] - 83s 7ms/step - loss: nan - accuracy: 0.1572 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 2/20

   64/12000 [..............................] - ETA: 1:16 - loss: nan - accuracy: 0.0000e+00
  128/12000 [..............................] - ETA: 1:15 - loss: nan - accuracy: 0.0000e+00
  192/12000 [..............................] - ETA: 1:16 - loss: nan - accuracy: 0.0000e+00
(...)
11840/12000 [============================>.] - ETA: 1s - loss: nan - accuracy: 0.0000e+00
11904/12000 [============================>.] - ETA: 0s - loss: nan - accuracy: 0.0000e+00
11968/12000 [============================>.] - ETA: 0s - loss: nan - accuracy: 0.0000e+00
12000/12000 [==============================] - 82s 7ms/step - loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
Epoch 3/20

   64/12000 [..............................] - ETA: 1:18 - loss: nan - accuracy: 0.0000e+00
  128/12000 [..............................] - ETA: 1:18 - loss: nan - accuracy: 0.0000e+00
  192/12000 [..............................] - ETA: 1:16 - loss: nan - accuracy: 0.0000e+00
(...)

定位问题

为了找出解决方案，我编写了下面给出的代码，该代码通过了几个模型（GRU/LSTM，没有 dropout，只有“正常”dropout，只有经常性 dropout，“正常”和经常性 dropout，rmsprop/adam ) 并呈现所有这些模型的损失和准确性。（它还为每个模型创建更小的、单独的图表。）

# Based on examples from "Deep Learning with Python" by François Chollet:
## Constants, modules:
VERSION = 2

import os
from keras import models
from keras import layers
import matplotlib.pyplot as plt
import pylab

## Loading data:
from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = \
    imdb.load_data(num_words=10000)

from keras.preprocessing import sequence
x_train = sequence.pad_sequences(x_train, maxlen=500)
x_test = sequence.pad_sequences(x_test, maxlen=500)


## Dictionary with models' hyperparameters:
MODELS = [
    # GRU:
    "no": 1,
     "layer_type": "GRU",
     "optimizer": "rmsprop",
     "dropout": None,
     "recurrent_dropout": None,

    "no": 2,
     "layer_type": "GRU",
     "optimizer": "rmsprop",
     "dropout": 0.3,
     "recurrent_dropout": None,

    "no": 3,
     "layer_type": "GRU",
     "optimizer": "rmsprop",
     "dropout": None,
     "recurrent_dropout": 0.3,

    "no": 4,
     "layer_type": "GRU",
     "optimizer": "rmsprop",
     "dropout": 0.3,
     "recurrent_dropout": 0.3,

    "no": 5,
     "layer_type": "GRU",
     "optimizer": "adam",
     "dropout": None,
     "recurrent_dropout": None,

    "no": 6,
     "layer_type": "GRU",
     "optimizer": "adam",
     "dropout": 0.3,
     "recurrent_dropout": None,

    "no": 7,
     "layer_type": "GRU",
     "optimizer": "adam",
     "dropout": None,
     "recurrent_dropout": 0.3,

    "no": 8,
     "layer_type": "GRU",
     "optimizer": "adam",
     "dropout": 0.3,
     "recurrent_dropout": 0.3,

    # LSTM:
    "no": 9,
     "layer_type": "LSTM",
     "optimizer": "rmsprop",
     "dropout": None,
     "recurrent_dropout": None,

    "no": 10,
     "layer_type": "LSTM",
     "optimizer": "rmsprop",
     "dropout": 0.3,
     "recurrent_dropout": None,

    "no": 11,
     "layer_type": "LSTM",
     "optimizer": "rmsprop",
     "dropout": None,
     "recurrent_dropout": 0.3,

    "no": 12,
     "layer_type": "LSTM",
     "optimizer": "rmsprop",
     "dropout": 0.3,
     "recurrent_dropout": 0.3,

    "no": 13,
     "layer_type": "LSTM",
     "optimizer": "adam",
     "dropout": None,
     "recurrent_dropout": None,

    "no": 14,
     "layer_type": "LSTM",
     "optimizer": "adam",
     "dropout": 0.3,
     "recurrent_dropout": None,

    "no": 15,
     "layer_type": "LSTM",
     "optimizer": "adam",
     "dropout": None,
     "recurrent_dropout": 0.3,

    "no": 16,
     "layer_type": "LSTM",
     "optimizer": "adam",
     "dropout": 0.3,
     "recurrent_dropout": 0.3,
]

## Adding name:
for model_dict in MODELS:
    model_dict["name"] = f"model_dict['layer_type']"
    model_dict["name"] += f"_dmodel_dict['dropout']" if model_dict['dropout'] is not None else f"_dN"
    model_dict["name"] += f"_rdmodel_dict['recurrent_dropout']" if model_dict['recurrent_dropout'] is not None else f"_rdN"
    model_dict["name"] += f"_model_dict['optimizer']"

## Fucntion - defing and training model:
def train_model(model_dict):
    """Defines and trains a model, outputs history."""

    ## Defining:
    model = models.Sequential()
    model.add(layers.Embedding(10000, 32))

    recurrent_layer_kwargs = dict()
    if model_dict["dropout"] is not None:
        recurrent_layer_kwargs["dropout"] = model_dict["dropout"]
    if model_dict["recurrent_dropout"] is not None:
        recurrent_layer_kwargs["recurrent_dropout"] = model_dict["recurrent_dropout"]

    if model_dict["layer_type"] == 'GRU':
        model.add(layers.GRU(32, **recurrent_layer_kwargs))
    elif model_dict["layer_type"] == 'LSTM':
        model.add(layers.LSTM(32, **recurrent_layer_kwargs))
    else:
        raise ValueError("Wrong model_dict['layer_type'] value...")
    model.add(layers.Dense(1, activation='sigmoid'))

    ## Compiling:
    model.compile(
        optimizer=model_dict["optimizer"],
        loss='binary_crossentropy',
        metrics=['accuracy'])

    ## Training:
    history = model.fit(x_train, y_train,
                        epochs=20,
                        batch_size=64,
                        validation_split=0.2)

    return history

## Multi-model graphs' parameters:
graph_all_nrow = 4
graph_all_ncol = 4
graph_all_figsize = (20, 20)

assert graph_all_nrow * graph_all_nrow >= len(MODELS)

## Figs and axes of multi-model graphs:
graph_all_loss_fig, graph_all_loss_axs = plt.subplots(graph_all_nrow, graph_all_ncol, figsize=graph_all_figsize)
graph_all_acc_fig, graph_all_acc_axs = plt.subplots(graph_all_nrow, graph_all_ncol, figsize=graph_all_figsize)

## Loop trough all models:
for i, model_dict in enumerate(MODELS):
    history = train_model(model_dict)

    ## Metrics extraction:
    loss = history.history['loss']
    val_loss = history.history['val_loss']
    acc = history.history['accuracy']
    val_acc = history.history['val_accuracy']

    epochs = range(1, len(loss) + 1)

    ## Single-model grph - loss:
    graph_loss_fname = fr"os.path.basename(__file__).replace('.py', '')"
    graph_loss_fname += fr"_vVERSION_model_dict['no']_model_dict['name']_loss_graph.png"

    graph_loss_fig, graph_loss_ax = plt.subplots()
    graph_loss_ax.plot(epochs, loss, 'bo', label='Training loss')
    graph_loss_ax.plot(epochs, val_loss, 'b', label='Validation loss')
    graph_loss_ax.legend()
    graph_loss_fig.suptitle("Training and validation loss")
    graph_loss_fig.savefig(graph_loss_fname)
    pylab.close(graph_loss_fig)


    ## Single-model grph - accuracy:
    graph_acc_fname = fr"os.path.basename(__file__).replace('.py', '')"
    graph_acc_fname += fr"_vVERSION_model_dict['no']_model_dict['name']_acc_graph.png"

    graph_acc_fig, graph_acc_ax = plt.subplots()
    graph_acc_ax.plot(epochs, acc, 'bo', label='Training accuracy')
    graph_acc_ax.plot(epochs, val_acc, 'b', label='Validation accuracy')
    graph_acc_ax.legend()
    graph_acc_fig.suptitle("Training and validation acc")
    graph_acc_fig.savefig(graph_acc_fname)
    pylab.close(graph_acc_fig)

    ## Position of axes on multi-model graph:
    i_row = i // graph_all_ncol
    i_col = i % graph_all_ncol

    ## Adding model metrics to multi-model graph - loss:
    graph_all_loss_axs[i_row, i_col].plot(epochs, loss, 'bo', label='Training loss')
    graph_all_loss_axs[i_row, i_col].plot(epochs, val_loss, 'b', label='Validation loss')
    graph_all_loss_axs[i_row, i_col].set_title(fr"model_dict['no']. model_dict['name']")

    ## Adding model metrics to multi-model graph - accuracy:
    graph_all_acc_axs[i_row, i_col].plot(epochs, acc, 'bo', label='Training acc')
    graph_all_acc_axs[i_row, i_col].plot(epochs, val_acc, 'b', label='Validation acc')
    graph_all_acc_axs[i_row, i_col].set_title(fr"model_dict['no']. model_dict['name']")


## Saving multi-model graphs:
# Output files are quite big (8000x8000 PNG), you may want to decrease DPI.
graph_all_loss_fig.savefig(fr"os.path.basename(__file__).replace('.py', '')_ALL_loss_graph.png", dpi=400)
graph_all_acc_fig.savefig(fr"os.path.basename(__file__).replace('.py', '')_ALL_acc_graph.png", dpi=400)

请在下面找到两个主要图表：Loss - binary crossentropy、Accuracy（由于声誉低，我不允许在帖子中嵌入图片）。

我在回归模型中也遇到了类似的奇怪问题 - MAE 在几千的范围内 - 在 $y$ 范围可能是几几十的问题中. （我决定不在这里包含这个模型，因为它会使这个问题变得更长。）

模块和库的版本、硬件

模块：

Keras                    2.3.1
Keras-Applications       1.0.8
Keras-Preprocessing      1.1.0
matplotlib               3.1.3
tensorflow-estimator     1.14.0
tensorflow-gpu           2.1.0
tensorflow-gpu-estimator 2.1.0

keras.json文件：


    "floatx": "float32",
    "epsilon": 1e-07,
    "backend": "tensorflow",
    "image_data_format": "channels_last"

CUDA - 我的系统上安装了 CUDA 10.0 和 CUDA 10.1。 CUDnn - 我有三个版本：cudnn-10.0 v7.4.2.24、cudnn-10.0 v7.6.4.38、cudnn-9.0 v7.4.2.24 GPU：Nvidia GTX 1050Ti 4gb Windows 10 家庭版

问题

您知道这种行为的可能原因是什么吗？这可能是由多个 CUDA 和 CUDnn 安装引起的吗？在观察问题之前，我已经训练了几个模型（来自书本和我自己的模型）并且似乎表现得或多或少符合预期，同时有 2 个 CUDA 和 2 个 CUDnn 版本（上面没有 cudnn-10.0 v7.6.4.38 的那些）已安装。是否有任何官方/良好的 keras、tensorflow、CUDA、CUDnn（以及其他相关内容，例如可能是 Visual Studio）的适当组合来源？我真的找不到任何权威和最新的来源。

我希望我已经足够清楚地描述了所有内容。如果您有任何问题，请提出。

【问题讨论】：

【参考方案1】：

我终于找到了解决方案（有点）。把keras改成tensorflow.keras就够了。

修改后的代码

# Based on examples from "Deep Learning with Python" by François Chollet:
## Constants, modules:
VERSION = 2

import os
#U: from keras import models
#U: from keras import layers
from tensorflow.keras import models
from tensorflow.keras import layers

import matplotlib.pyplot as plt
import pylab

## Loading data:
from keras.datasets import imdb

(x_train, y_train), (x_test, y_test) = \
    imdb.load_data(num_words=10000)

from keras.preprocessing import sequence

x_train = sequence.pad_sequences(x_train, maxlen=500)
x_test = sequence.pad_sequences(x_test, maxlen=500)

## Dictionary with models' hyperparameters:
MODELS_ALL = [
    # GRU:
    "no": 1,
     "layer_type": "GRU",
     "optimizer": "rmsprop",
     "dropout": None,
     "recurrent_dropout": None,

    "no": 2,
     "layer_type": "GRU",
     "optimizer": "rmsprop",
     "dropout": 0.3,
     "recurrent_dropout": None,

    "no": 3,
     "layer_type": "GRU",
     "optimizer": "rmsprop",
     "dropout": None,
     "recurrent_dropout": 0.3,

    "no": 4,
     "layer_type": "GRU",
     "optimizer": "rmsprop",
     "dropout": 0.3,
     "recurrent_dropout": 0.3,

    "no": 5,
     "layer_type": "GRU",
     "optimizer": "adam",
     "dropout": None,
     "recurrent_dropout": None,

    "no": 6,
     "layer_type": "GRU",
     "optimizer": "adam",
     "dropout": 0.3,
     "recurrent_dropout": None,

    "no": 7,
     "layer_type": "GRU",
     "optimizer": "adam",
     "dropout": None,
     "recurrent_dropout": 0.3,

    "no": 8,
     "layer_type": "GRU",
     "optimizer": "adam",
     "dropout": 0.3,
     "recurrent_dropout": 0.3,

    # LSTM:
    "no": 9,
     "layer_type": "LSTM",
     "optimizer": "rmsprop",
     "dropout": None,
     "recurrent_dropout": None,

    "no": 10,
     "layer_type": "LSTM",
     "optimizer": "rmsprop",
     "dropout": 0.3,
     "recurrent_dropout": None,

    "no": 11,
     "layer_type": "LSTM",
     "optimizer": "rmsprop",
     "dropout": None,
     "recurrent_dropout": 0.3,

    "no": 12,
     "layer_type": "LSTM",
     "optimizer": "rmsprop",
     "dropout": 0.3,
     "recurrent_dropout": 0.3,

    "no": 13,
     "layer_type": "LSTM",
     "optimizer": "adam",
     "dropout": None,
     "recurrent_dropout": None,

    "no": 14,
     "layer_type": "LSTM",
     "optimizer": "adam",
     "dropout": 0.3,
     "recurrent_dropout": None,

    "no": 15,
     "layer_type": "LSTM",
     "optimizer": "adam",
     "dropout": None,
     "recurrent_dropout": 0.3,

    "no": 16,
     "layer_type": "LSTM",
     "optimizer": "adam",
     "dropout": 0.3,
     "recurrent_dropout": 0.3,
]

MODELS_GRU_RECCURENT = [
    # GRU:
    "no": 3,
     "layer_type": "GRU",
     "optimizer": "rmsprop",
     "dropout": None,
     "recurrent_dropout": 0.3,

    "no": 4,
     "layer_type": "GRU",
     "optimizer": "rmsprop",
     "dropout": 0.3,
     "recurrent_dropout": 0.3,

    "no": 7,
     "layer_type": "GRU",
     "optimizer": "adam",
     "dropout": None,
     "recurrent_dropout": 0.3,

    "no": 8,
     "layer_type": "GRU",
     "optimizer": "adam",
     "dropout": 0.3,
     "recurrent_dropout": 0.3,
]

MODELS = MODELS_ALL   # "MODELS = MODELS_ALL" or "MODELS = MODELS_GRU_RECCURENT"

## Adding name:
for model_dict in MODELS:
    model_dict["name"] = f"model_dict['layer_type']"
    model_dict["name"] += f"_dmodel_dict['dropout']" if model_dict['dropout'] is not None else f"_dN"
    model_dict["name"] += f"_rdmodel_dict['recurrent_dropout']" if model_dict['recurrent_dropout'] is not None else f"_rdN"
    model_dict["name"] += f"_model_dict['optimizer']"


## Fucntion - defing and training model:
def train_model(model_dict):
    """Defines and trains a model, outputs history."""

    ## Defining:
    model = models.Sequential()
    model.add(layers.Embedding(10000, 32))

    recurrent_layer_kwargs = dict()
    if model_dict["dropout"] is not None:
        recurrent_layer_kwargs["dropout"] = model_dict["dropout"]
    if model_dict["recurrent_dropout"] is not None:
        recurrent_layer_kwargs["recurrent_dropout"] = model_dict["recurrent_dropout"]

    if model_dict["layer_type"] == 'GRU':
        model.add(layers.GRU(32, **recurrent_layer_kwargs))
    elif model_dict["layer_type"] == 'LSTM':
        model.add(layers.LSTM(32, **recurrent_layer_kwargs))
    else:
        raise ValueError("Wrong model_dict['layer_type'] value...")
    model.add(layers.Dense(1, activation='sigmoid'))

    ## Compiling:
    model.compile(
        optimizer=model_dict["optimizer"],
        loss='binary_crossentropy',
        metrics=['accuracy'])

    ## Training:
    history = model.fit(x_train, y_train,
                        epochs=20,
                        batch_size=64,
                        validation_split=0.2)

    return history


## Multi-model graphs' parameters:
graph_all_nrow = 4
graph_all_ncol = 4
graph_all_figsize = (20, 20)

assert graph_all_nrow * graph_all_nrow >= len(MODELS)

# fig and axes of multi-model graphs:
graph_all_loss_fig, graph_all_loss_axs = plt.subplots(graph_all_nrow, graph_all_ncol, figsize=graph_all_figsize)
graph_all_acc_fig, graph_all_acc_axs = plt.subplots(graph_all_nrow, graph_all_ncol, figsize=graph_all_figsize)

## Loop trough all models:
for i, model_dict in enumerate(MODELS):
    history = train_model(model_dict)

    ## Metrics extraction:
    loss = history.history['loss']
    val_loss = history.history['val_loss']
    acc = history.history['accuracy']
    val_acc = history.history['val_accuracy']

    epochs = range(1, len(loss) + 1)

    ## Single-model graph - loss:
    graph_loss_fname = fr"os.path.basename(__file__).replace('.py', '')"
    graph_loss_fname += fr"_vVERSION_model_dict['no']_model_dict['name']_loss_graph.png"

    graph_loss_fig, graph_loss_ax = plt.subplots()
    graph_loss_ax.plot(epochs, loss, 'bo', label='Training loss')
    graph_loss_ax.plot(epochs, val_loss, 'b', label='Validation loss')
    graph_loss_ax.legend()
    graph_loss_fig.suptitle("Training and validation loss")
    graph_loss_fig.savefig(graph_loss_fname)
    pylab.close(graph_loss_fig)

    ## Single-model graph - accuracy:
    graph_acc_fname = fr"os.path.basename(__file__).replace('.py', '')"
    graph_acc_fname += fr"_vVERSION_model_dict['no']_model_dict['name']_acc_graph.png"

    graph_acc_fig, graph_acc_ax = plt.subplots()
    graph_acc_ax.plot(epochs, acc, 'bo', label='Training accuracy')
    graph_acc_ax.plot(epochs, val_acc, 'b', label='Validation accuracy')
    graph_acc_ax.legend()
    graph_acc_fig.suptitle("Training and validation acc")
    graph_acc_fig.savefig(graph_acc_fname)
    pylab.close(graph_acc_fig)

    ## Position of axes on multi-model graph:
    i_row = i // graph_all_ncol
    i_col = i % graph_all_ncol

    ## Adding model metrics to multi-model graph - loss:
    graph_all_loss_axs[i_row, i_col].plot(epochs, loss, 'bo', label='Training loss')
    graph_all_loss_axs[i_row, i_col].plot(epochs, val_loss, 'b', label='Validation loss')
    graph_all_loss_axs[i_row, i_col].set_title(fr"model_dict['no']. model_dict['name']")

    ## Adding model metrics to multi-model graph - accuracy:
    graph_all_acc_axs[i_row, i_col].plot(epochs, acc, 'bo', label='Training acc')
    graph_all_acc_axs[i_row, i_col].plot(epochs, val_acc, 'b', label='Validation acc')
    graph_all_acc_axs[i_row, i_col].set_title(fr"model_dict['no']. model_dict['name']")

graph_all_loss_fig.suptitle(f"Loss - binary crossentropy [vVERSION]")
graph_all_acc_fig.suptitle(f"Accuracy [vVERSION]")

## Saving multi-model graphs:
graph_all_loss_fig.savefig(fr"os.path.basename(__file__).replace('.py', '')_ALL_vVERSION_loss_graph.png", dpi=400)
graph_all_acc_fig.savefig(fr"os.path.basename(__file__).replace('.py', '')_ALL_vVERSION_acc_graph.png", dpi=400)

## Saving multi-model graphs (SMALL):
graph_all_loss_fig.savefig(fr"os.path.basename(__file__).replace('.py', '')_ALL_vVERSION_loss_graph_SMALL.png", dpi=150)
graph_all_acc_fig.savefig(fr"os.path.basename(__file__).replace('.py', '')_ALL_vVERSION_acc_graph_SMALL.png", dpi=150)

结果

与这些问题类似的图表：Loss - binary crossentropy、Accuracy

更多关于 `keras` 与 `tensorflow.keras`

正如 François Chollet 的 tweets（可在此处找到：https://***.com/a/54117754）中所写，从现在开始将使用 keras 而非独立的 tensorflow.keras（即 Keras 作为 TensorFlow 的官方 API）。（我不完全确定我是否 100% 正确，请随时纠正我。）

我认为在以后的项目中使用tensorflow.keras 而不是keras 会更好。

【讨论】：

谢谢。我正在开发一个 Azure 机器学习 Ubuntu Linux VM，Keras 2.3.1。更改为 tensorflow.keras 而不是 keras 给了我与本书相似的结果。【参考方案2】：

在使用 Keras 的 R 接口进行培训时，我也是如此。这个问题似乎与经常性辍学和“时间”维度的长度有关。它仅使用 GRU 发生（lstm 没有问题）。

# remotes::install_github("rstudio/keras#1032")
library(keras)


reticulate::py_config()
#> python:         /home/clanera/anaconda3/envs/r-tensorflow/bin/python
#> libpython:      /home/clanera/anaconda3/envs/r-tensorflow/lib/libpython3.6m.so
#> pythonhome:     /home/clanera/anaconda3/envs/r-tensorflow:/home/clanera/anaconda3/envs/r-tensorflow
#> version:        3.6.10 |Anaconda, Inc.| (default, Jan  7 2020, 21:14:29)  [GCC 7.3.0]
#> numpy:          /home/clanera/anaconda3/envs/r-tensorflow/lib/python3.6/site-packages/numpy
#> numpy_version:  1.18.1
#> tensorflow:     /home/clanera/anaconda3/envs/r-tensorflow/lib/python3.6/site-packages/tensorflow
#> 
#> NOTE: Python version was forced by RETICULATE_PYTHON
tensorflow::tf_config()
#> TensorFlow v2.0.0 (~/anaconda3/envs/r-tensorflow/lib/python3.6/site-packages/tensorflow)
#> Python v3.6 (~/anaconda3/envs/r-tensorflow/bin/python)
tensorflow::tf_gpu_configured()
#> TensorFlow built with CUDA:  FALSE 
#> GPU device name:
#> [1] FALSE


n <- 100
t <- 80 # with 72- seams have no problem
q <- 10

x <- array(sample(n*t*q), c(n, t, q))
y <- sample(0:1, n, replace = TRUE)


input <- layer_input(c(t, q))
output <- input %>% 
#  ## no problem using LSTM
#  layer_lstm(units = 2, recurrent_dropout = 0.5) %>%
  layer_gru(units = 2, recurrent_dropout = 0.5) %>%
  layer_dense(units = 1, activation = "sigmoid")

model <- keras_model(input, output)

summary(model)
#> Model: "model"
#> ________________________________________________________________________________
#> Layer (type)                        Output Shape                    Param #     
#> ================================================================================
#> input_1 (InputLayer)                [(None, 80, 10)]                0           
#> ________________________________________________________________________________
#> gru (GRU)                           (None, 2)                       78          
#> ________________________________________________________________________________
#> dense (Dense)                       (None, 1)                       3           
#> ================================================================================
#> Total params: 81
#> Trainable params: 81
#> Non-trainable params: 0
#> ________________________________________________________________________________

history <- model %>%
  compile(optimizer = "adam", loss = "binary_crossentropy") %>% 
  fit(x, y, 2, 3)

history
#> Trained on 100 samples (batch_size=2, epochs=3)
#> Final epoch (plot to see history):
#> loss: NaN

^{由reprex package (v0.3.0) 于 2020-05-10 创建}

sessionInfo()
#> R version 4.0.0 (2020-04-24)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.4 LTS
#> 
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices datasets  utils     methods   base     
#> 
#> other attached packages:
#> [1] keras_2.2.5.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] Rcpp_1.0.4.6         whisker_0.4          knitr_1.28          
#>  [4] magrittr_1.5         lattice_0.20-41      R6_2.4.1            
#>  [7] rlang_0.4.6          stringr_1.4.0        highr_0.8           
#> [10] tools_4.0.0          grid_4.0.0           xfun_0.13           
#> [13] htmltools_0.4.0      tfruns_1.4           yaml_2.2.1          
#> [16] digest_0.6.25        tensorflow_2.0.0     Matrix_1.2-18       
#> [19] base64enc_0.1-3      zeallot_0.1.0        evaluate_0.14       
#> [22] rmarkdown_2.1        stringi_1.4.6        compiler_4.0.0      
#> [25] generics_0.0.2       reticulate_1.15-9000 jsonlite_1.6.1      
#> [28] renv_0.10.0

【讨论】：

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