Ошибка недопустимого аргумента/ошибка выполнения графика ⇐ Python

[Anonymous]

При выполнении этого обучающего кода VGG у меня возникает несколько ошибок (код и ошибки показаны ниже). Я не знаю, связано ли это с моим набором данных или с чем-то другим.

Код: Выделить всё

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics.pairwise import cosine_similarity
import os
import scipy

train_directory = 'sign_data/train' #To be changed
test_directory = 'sign_data/test' #To be changed

train_datagen = ImageDataGenerator(
rescale = 1./255,
rotation_range = 0.1,
width_shift_range = 0.2,
height_shift_range = 0.2,
shear_range = 0.1
)

train_generator = train_datagen.flow_from_directory(
train_directory,
target_size = (224, 224),
color_mode = 'rgb',
shuffle = True,
batch_size=32

)

test_datagen = ImageDataGenerator(
rescale = 1./255,
)

test_generator = test_datagen.flow_from_directory(
test_directory,
target_size = (224, 224),
color_mode = 'rgb',
shuffle = True,
batch_size=32
)

from tensorflow.keras.applications.vgg16 import VGG16
vgg_basemodel = VGG16(include_top=True)

from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping

early_stopping = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)

vgg_model = tf.keras.Sequential(vgg_basemodel.layers[:-1])
vgg_model.add(tf.keras.layers.Dense(10, activation = 'softmax'))

# Freezing original layers
for layer in vgg_model.layers[:-1]:
layer.trainable = False

vgg_model.compile(loss='categorical_crossentropy',
optimizer=tf.keras.optimizers.SGD(momentum=0.9, learning_rate=0.001, decay=0.01),
metrics=['accuracy'])

history = vgg_model.fit(train_generator,
epochs=30,
batch_size=64,
validation_data=test_generator,
callbacks=[early_stopping])

# finetuning with all layers set trainable

for layer in vgg_model.layers:
layer.trainable = True

vgg_model.compile(loss='categorical_crossentropy',
optimizer=tf.keras.optimizers.SGD(momentum=0.9, lr=0.0001),
metrics=['accuracy'])

history2 = vgg_model.fit(train_generator,
epochs=5,
batch_size=64,
validation_data=test_generator,
callbacks=[early_stopping])

vgg_model.save('saved_models/vgg_finetuned_model')

Первая ошибка: ошибка недопустимого аргумента

Код: Выделить всё

    InvalidArgumentError                      Traceback (most recent call last)
 in ()
14               batch_size=64,
15               validation_data=test_generator,
---> 16               callbacks=[early_stopping])
17
18 # finetuning with all layers set trainable

/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
65     except Exception as e:  # pylint: disable=broad-except
66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
68     finally:
69       del filtered_tb

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
53     ctx.ensure_initialized()
54     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
--->  55                                         inputs, attrs, num_outputs)
56   except core._NotOkStatusException as e:
57     if name is not None:

Вторая ошибка: ошибка выполнения графика

Код: Выделить всё

    InvalidArgumentError:  Graph execution error:
Detected at node 'categorical_crossentropy/softmax_cross_entropy_with_logits' defined at (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in 
app.launch_new_instance()
File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
app.start()
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 499, in start
self.io_loop.start()
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
self._run_once()
File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
handle._run()
File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 122, in _handle_events
handler_func(fileobj, events)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 452, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 481, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 431, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "", line 16, in 
callbacks=[early_stopping])
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 919,  in compute_loss
y, y_pred, sample_weight, regularization_losses=self.losses)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1790, in categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)
File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 5099, in categorical_crossentropy
labels=target, logits=output, axis=axis)
Node: 'categorical_crossentropy/softmax_cross_entropy_with_logits'
logits and labels must be broadcastable: logits_size=[32,10] labels_size=[32,128]
[[{{node categorical_crossentropy/softmax_cross_entropy_with_logits}}]] [Op:__inference_train_function_11227]

Я запускаю это в совместной лаборатории Google. Есть ли модуль, который мне следует установить? Или это чисто ошибка самого кода?

Подробнее здесь: https://stackoverflow.com/questions/711 ... tion-error