如何使用pyinstaller或cx_freeze打包包含traitsui、tensorflow和matplotlib的python脚本？

我正在寻找将以下代码分发到Windows 10计算机的方法：

""" Neural Network with Eager API.

A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron)
implementation with TensorFlow's Eager API. This example is using the MNIST database
of handwritten digits (http://yann.lecun.com/exdb/mnist/).

This example is using TensorFlow layers, see 'neural_network_raw' example for
a raw implementation with variables.

Links:
    [MNIST Dataset](http://yann.lecun.com/exdb/mnist/).

Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
"""
from __future__ import print_function

import tensorflow as tf
import tensorflow.contrib.eager as tfe
from datetime import datetime

from traits.api import HasTraits, CInt, CFloat
from traitsui.api import View, Item, Handler, Action

import matplotlib.pyplot as plt
import os
print('1') #Added for debugging along with the input, doesn't work.
input()

# Set Eager API
tfe.enable_eager_execution()
print('2')
input()
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)

# Parameters
learning_rate = 0.001
num_steps = 5
batch_size = 128
display_step = 10

# OODA tries
eps = 0.002
lower_bound = 0.005
upper_bound = 100
ooda_step = 5
epochs = 50

# Network Parameters
n_hidden_1 = 256 # 1st layer number of neurons
n_hidden_2 = 256 # 2nd layer number of neurons
num_input = 784 # MNIST data input (img shape: 28*28)
num_classes = 10 # MNIST total classes (0-9 digits)

# Using TF Dataset to split data into batches
dataset = tf.data.Dataset.from_tensor_slices(
    (mnist.train.images, mnist.train.labels)).batch(batch_size)
dataset_iter = tfe.Iterator(dataset)

# Define the neural network. To use eager API and tf.layers API together,
# we must instantiate a tfe.Network class as follow:
class NeuralNet(tfe.Network):

    # SGD Optimizer
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
    # Compute gradients
    #grad = 0# = tfe.implicit_gradients(NeuralNet.loss_fn)
    def __init__(self):
        # Define each layer
        super(NeuralNet, self).__init__()
        # Hidden fully connected layer with 256 neurons
        self.layer1 = self.track_layer(
            tf.layers.Dense(n_hidden_1, activation=tf.nn.relu))
        # Hidden fully connected layer with 256 neurons
        self.layer2 = self.track_layer(
            tf.layers.Dense(n_hidden_2, activation=tf.nn.relu))
        # Output fully connected layer with a neuron for each class
        self.out_layer = self.track_layer(tf.layers.Dense(num_classes))
        NeuralNet.grad = tfe.implicit_gradients(NeuralNet.loss_fn)

    def call(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        return self.out_layer(x)

    # Cross-Entropy loss function
    @staticmethod
    def loss_fn(inference_fn, inputs, labels):
        # Using sparse_softmax cross entropy
        return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
            logits=inference_fn(inputs), labels=labels))


    # Calculate accuracy
    @staticmethod    
    def accuracy_fn(inference_fn, inputs, labels):
        prediction = tf.nn.softmax(inference_fn(inputs))
        correct_pred = tf.equal(tf.argmax(prediction, 1), labels)
        return tf.reduce_mean(tf.cast(correct_pred, tf.float32))

    @staticmethod
    def trainNN():
        testId = 0
        #files
        if os.path.exists('tests.txt'):
            #get id from file
            file = open('tests.txt', 'r')
            lines = file.read().splitlines()
            if lines != []:
                testId = int(lines[-1].split()[0]) + 1
            file.close()

        logs = open('logs.txt', 'a+')
        graphslog = open('graphs.txt', 'a+')
        testslog = open('tests.txt', 'a+')

        # Training
        lossevol = []
        accevol = []
        dataset_iter = tfe.Iterator(dataset)
        neural_net = NeuralNet()
        average_loss = 0.
        average_acc = 0.
        start = datetime.now()
        for epoch in range(epochs):
            for step in range(num_steps):

                # Iterate through the dataset
                try:
                    d = dataset_iter.next()
                except StopIteration:
                    # Refill queue
                    dataset_iter = tfe.Iterator(dataset)
                    d = dataset_iter.next()

                # Images
                x_batch = d[0]
                # Labels
                y_batch = tf.cast(d[1], dtype=tf.int64)

                # Compute the batch loss
                batch_loss = NeuralNet.loss_fn(neural_net, x_batch, y_batch)
                average_loss += batch_loss
                # Compute the batch accuracy
                batch_accuracy = NeuralNet.accuracy_fn(neural_net, x_batch, y_batch)
                average_acc += batch_accuracy

                if step == 0:
                    # Display the initial cost, before optimizing
                    print("Initial loss= {:.9f}".format(average_loss))
                    logs.write("Initial loss= {:.9f}\n".format(average_loss))

                # Update the variables following gradients info
                if epoch < ooda> lower_bound and batch_loss < upper> ooda_step):
                    NeuralNet.optimizer.apply_gradients(NeuralNet.grad(neural_net, x_batch, y_batch))

                # Display info
                if (step + 1) % display_step == 0 or step == 0:
                    if step > 0:
                        average_loss /= display_step
                        average_acc /= display_step
                    lossevol.append(average_loss)
                    accevol.append(batch_accuracy)
                    print("Epoch:", 'd' % (epoch + 1),
                          "Step:", 'd' % (step + 1), " loss=",
                          "{:.9f}".format(average_loss), " accuracy=",
                          "{:.4f}".format(average_acc))
                    logs.write('Epoch: d ' % (epoch + 1) +
                       'Step: d ' % (step + 1) + 
                       'loss={:.9f} '.format(average_loss) +
                       'accuracy={:.4f}\n'.format(average_acc))
                    average_loss = 0.
                    average_acc = 0.

        end = datetime.now()
        print("Training took: ", (end - start), logs)
        trainingTime = end-start
        # Evaluate model on the test image set
        testX = mnist.test.images
        testY = mnist.test.labels


        test_acc = NeuralNet.accuracy_fn(neural_net, testX, testY)
        print("Testset Accuracy: {:.4f}".format(test_acc)) # 9694 9753 9763 9783 9762
        logs.write("Testset Accuracy: {:.4f}\n".format(test_acc))

        aux = range(len(lossevol))
        plt.figure()
        plt.xlabel("Display steps")
        plt.ylabel("Loss")
        plt.plot(aux, lossevol, color = "red")
        plt.show()
        plt.figure()
        plt.xlabel("Display steps")
        plt.ylabel("Batch Accuaracy")
        plt.plot(aux, accevol , color = "blue")
        plt.show()
        logs.write("-----------------------------------------\n")

        print("%d " % testId + "{:.4f} ".format(test_acc) + "\n")        
        graphslog.write("%d " % testId + "{:.4f} ".format(test_acc) + str(trainingTime) + "\n")
        testslog.write("%d " % testId + 
                       "Epochs: %d " % epochs +
                       "Steps number: %d " % num_steps +
                       "Batch size: %d " % batch_size +
                       "OODA step: %d " % ooda_step +
                       "Lower bound: %f " % lower_bound +
                       "Upper bound: %f " % upper_bound +
                       "Display step: %d\n" % display_step)
        logs.close()
        graphslog.close()
        testslog.close()

#GUI Configuration

class NNHandler(Handler):

    def setattr(self, info, object, name, value):
        Handler.setattr(self, info, object, name, value)
        info.object._updated = True

    def object__updated_changed(self, info):
        if info.initialized:
            info.ui.title += "*"
            #info.ui.camera.epochs = 500

    def test(self, info):
        global epochs
        global num_steps
        global batch_size
        global ooda_step
        global lower_bound
        global upper_bound
        global display_step
        epochs = info.epochs.value
        num_steps = info.num_steps.value
        batch_size = info.batch_size.value
        ooda_step = info.ooda_step.value
        lower_bound = info.lower_bound.value
        upper_bound = info.upper_bound.value
        display_step = info.display_step.value
        NeuralNet.trainNN()

    def graphs(self, info):
        if os.path.exists('graphs.txt') == False:
            return
        file = open('graphs.txt', 'r')
        tests = file.read().splitlines()

        test_num = range(len(tests))
        test_acc = []
        test_time = []

        for line in tests:
            words = line.split()
            test_acc.append(float(words[1]))
            test_time.append((datetime.strptime(words[2], '%H:%M:%S.%f') - datetime.strptime('1900 1 1', '%Y %m %d')).total_seconds())

        print(test_num)
        print(test_acc)
        print(test_time)
        plt.figure()
        plt.xlabel("Test ID")
        plt.ylabel("Time")
        plt.scatter(test_num, test_time, color = "red")
        plt.show()
        plt.figure()
        plt.xlabel("Test ID")
        plt.ylabel("Accuaracy")
        plt.scatter(test_num, test_acc , color = "blue")
        plt.show()


class Camera(HasTraits):
    epochs = CInt(50, label = "Epochs")
    num_steps = CInt(5, label = "Number of steps")
    batch_size = CInt(128, label = "Batch size")

    ooda_step = CInt(2, label = "OODA step")
    lower_bound = CFloat(0.005, label = "Lower bound")
    upper_bound = CFloat(100, label = "Upper bound")

    display_step = CInt(10, label = "Display step")

    train = Action(name = "Run NN",
                action = "test")

    generateGraph = Action(name = "Generate graphs",
                action = "graphs")

    view = View(
                Item('epochs'),
                Item('num_steps'),
                Item('batch_size'),
                Item('ooda_step'),
                Item('lower_bound'),
                Item('upper_bound'),
                Item('display_step'),
#                Item('figure', editor=MPLFigureEditor(),
#                                show_label=False),
                handler = NNHandler(),
                buttons = [train, generateGraph]
            )

cam = Camera()
#cam.configure_traits()

if __name__ == "__main__":
    print("wtf")
    cam.configure_traits()


input()

这将生成exe，但没有错误，但在尝试使用它时，我得到：

Intel MKL致命错误：无法加载MKL\u Intel\u thread.dll。

只需使用pyinstaller

同样，在构建时没有错误。当我打开控制台时，我实际上得到了一个错误

Traceback (most recent call last):
  File "ooda.py", line 23, in <module>
  File "<frozen>", line 971, in _find_and_load
  File "<frozen>", line 955, in _find_and_load_unlocked
  File "<frozen>", line 665, in _load_unlocked
  File "C:\Users\Roby\Anaconda3\envs\tensorflow\lib\site-packages\PyInstaller\loader\pyimod03_importers.py", line 631, in exec_module
    exec&#40;bytecode, module.__dict__&#41;
  File "site-packages\traitsui\api.py", line 36, in <module>
  File "<frozen>", line 971, in _find_and_load
  File "<frozen>", line 955, in _find_and_load_unlocked
  File "<frozen>", line 665, in _load_unlocked
  File "C:\Users\Roby\Anaconda3\envs\tensorflow\lib\site-packages\PyInstaller\loader\pyimod03_importers.py", line 631, in exec_module
    exec&#40;bytecode, module.__dict__&#41;
  File "site-packages\traitsui\editors\__init__.py", line 23, in <module>
  File "<frozen>", line 971, in _find_and_load
  File "<frozen>", line 955, in _find_and_load_unlocked
  File "<frozen>", line 665, in _load_unlocked
  File "C:\Users\Roby\Anaconda3\envs\tensorflow\lib\site-packages\PyInstaller\loader\pyimod03_importers.py", line 631, in exec_module
    exec&#40;bytecode, module.__dict__&#41;
  File "site-packages\traitsui\editors\api.py", line 24, in <module>
  File "<frozen>", line 971, in _find_and_load
  File "<frozen>", line 955, in _find_and_load_unlocked
  File "<frozen>", line 665, in _load_unlocked
  File "C:\Users\Roby\Anaconda3\envs\tensorflow\lib\site-packages\PyInstaller\loader\pyimod03_importers.py", line 631, in exec_module
    exec&#40;bytecode, module.__dict__&#41;
  File "site-packages\traitsui\editors\code_editor.py", line 37, in <module>
  File "site-packages\traitsui\editors\code_editor.py", line 49, in ToolkitEditorFactory
  File "site-packages\traits\traits.py", line 522, in __call__
  File "site-packages\traits\traits.py", line 1236, in Color
  File "site-packages\traitsui\toolkit_traits.py", line 8, in ColorTrait
  File "site-packages\traitsui\toolkit.py", line 109, in toolkit
  File "site-packages\pyface\base_toolkit.py", line 281, in find_toolkit
  File "site-packages\pyface\base_toolkit.py", line 209, in import_toolkit
RuntimeError: No traitsui.toolkits plugin found for toolkit null
[9148] Failed to execute script ooda

只需添加这两个变量，错误就会变为

No traitsui.toolkits plugin found for toolkit qt4

。 如果我也去设定

from traits.etsconfig.api import ETSConfig
ETSConfig.toolkit = 'pyqt'

找不到工具箱qt5的插件

因此，在进一步研究之后，我尝试通过以下方式导入pyfaces：

os.environ['ETS_TOOLKIT'] = 'qt4'

import imp
try:
    imp.find_module('PySide') # test if PySide if available
except ImportError:
    os.environ['QT_API'] = 'pyqt' # signal to pyface that PyQt4 should be used

在本例中，我得到了与qt4相同的错误

编辑#2

按照评论中的建议，我尝试显式地导入traitsui.qt4.toolkit，但即使这样，我也会遇到同样的错误，只是对于pyfaces，而不是带有pyinstaller的traitsui

另外，我注意到pyinstaller在构建时给了我一些警告：

102773 INFO: Looking for dynamic libraries
102980 WARNING: lib not found: mpich2mpi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_mpich2_ilp64.dll
103690 WARNING: lib not found: impi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_intelmpi_lp64.dll
105931 WARNING: lib not found: mpich2mpi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_mpich2_lp64.dll
106129 WARNING: lib not found: msmpi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_msmpi_ilp64.dll
106356 WARNING: lib not found: msmpi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_msmpi_lp64.dll
107704 WARNING: lib not found: impi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_intelmpi_ilp64.dll
109251 WARNING: lib not found: pgc14.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_pgi_thread.dll
109441 WARNING: lib not found: pgf90rtl.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_pgi_thread.dll
109639 WARNING: lib not found: pgf90.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_pgi_thread.dll
111431 WARNING: lib not found: tbb.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_tbb_thread.dll

经过一些研究，我发现了以下链接：

cx_freeze给了我一个可执行文件，它可以在没有任何消息的情况下立即关闭，而pyinstaller一直给我同样的错误

我的环境：

Windows 10 x64

Python 3.6

Tensorflow关于anaconda的最新消息（不确定）

Matplotlib 2.x

特征4.x

Traitsui 6.0.0

---

请注意，StackOverflow问题应该包括问题本身内部需要回答的所有内容，因此即使外部链接中断或更改，它仍然有意义。链接是受欢迎的，但前提是没有链接，问题仍然完整且可以回答。向问题本身添加代码时，请使用编辑界面中的

{}

按钮对其进行格式化。顺便说一句，你复习了吗？@CharlesDuffy你看，我已经编辑了问题。是的，这个问题我已经复习了好几遍了。请记住，这是Pyinstaller的错误，我在cx_冻结时遇到了这个错误。我在pyinstaller中遇到的错误是完全不同的。谢谢——添加的内容很有帮助。（理想情况下，我们应该有一个——可能导致相同错误的最短可运行代码——但这已经足够好了）。这通常意味着pyinstaller没有发现某个运行时导入——如果可以跟踪它是什么，可以手动为它添加显式导入或挂钩。请注意，

pyface.ui.null

实际上是一个真正存在的包。为它添加一个显式的

import

，然后用pyinstaller重新构建包可能就是您所需要的。

os.environ['ETS_TOOLKIT'] = 'qt4'

import imp
try:
    imp.find_module('PySide') # test if PySide if available
except ImportError:
    os.environ['QT_API'] = 'pyqt' # signal to pyface that PyQt4 should be used

102773 INFO: Looking for dynamic libraries
102980 WARNING: lib not found: mpich2mpi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_mpich2_ilp64.dll
103690 WARNING: lib not found: impi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_intelmpi_lp64.dll
105931 WARNING: lib not found: mpich2mpi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_mpich2_lp64.dll
106129 WARNING: lib not found: msmpi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_msmpi_ilp64.dll
106356 WARNING: lib not found: msmpi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_msmpi_lp64.dll
107704 WARNING: lib not found: impi.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_blacs_intelmpi_ilp64.dll
109251 WARNING: lib not found: pgc14.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_pgi_thread.dll
109441 WARNING: lib not found: pgf90rtl.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_pgi_thread.dll
109639 WARNING: lib not found: pgf90.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_pgi_thread.dll
111431 WARNING: lib not found: tbb.dll dependency of C:\Users\Roby\Anaconda3\envs\tensorflow\Library\bin\mkl_tbb_thread.dll