以下代码用于使用Tensorflow和Keras使用CNN模型进行疾病检测。出于某种原因，我不断收到错误消息。这是带有参数'inputs'的TypeError。我不明白为什么会出现此错误。这是我的代码：
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np # linear algebra
import pandas as pd # data processing CSV file
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
import cv2
import matplotlib.pyplot as plt
import seaborn as sns # seaborn is a data visualization library for python graphs
from PIL import Image
import os #file path interacting with operating system
thisFolder = os.path.dirname(os.path.realpath(__file__))
print(thisFolder)
print(tf.__version__)
infected = os.listdir(thisFolder + '/cell_images/cell_images/Parasitized/')
uninfected = os.listdir(thisFolder +'/cell_images/cell_images/Uninfected/')
data = []
labels = []
for i in infected:
try:
image = cv2.imread(thisFolder + "/cell_images/cell_images/Parasitized/"+i)
image_array = Image.fromarray(image , 'RGB')
resize_img = image_array.resize((50 , 50))
rotated45 = resize_img.rotate(45)
rotated75 = resize_img.rotate(75)
blur = cv2.blur(np.array(resize_img) ,(10, 10))
data.append(np.array(resize_img))
data.append(np.array(rotated45))
data.append(np.array(rotated75))
data.append(np.array(blur))
labels.append(1)
labels.append(1)
labels.append(1)
labels.append(1)
except AttributeError:
print('')
for u in uninfected:
try:
image = cv2.imread("../input/cell_images/cell_images/Uninfected/"+u)
image_array = Image.fromarray(image , 'RGB')
resize_img = image_array.resize((50 , 50))
rotated45 = resize_img.rotate(45)
rotated75 = resize_img.rotate(75)
data.append(np.array(resize_img))
data.append(np.array(rotated45))
data.append(np.array(rotated75))
labels.append(0)
labels.append(0)
labels.append(0)
except AttributeError:
print('')
cells = np.array(data)
labels = np.array(labels)
np.save('Cells' , cells)
np.save('Labels' , labels)
print('Cells : {} | labels : {}'.format(cells.shape , labels.shape))
# plt.figure(1 , figsize = (15, 9)) # all graphs and displays
n = 0
for i in range(49):
n += 1
r = np.random.randint(0 , cells.shape[0] , 1)
plt.subplot(7 , 7, n)
plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)
plt.imshow(cells[r[0]])
plt.title('{} : {}'.format('Infected' if labels[r[0]] == 1 else 'Uninfected', labels[r[0]]))
plt.xticks([]) , plt.yticks([])
plt.figure(1, figsize = (15 , 7))
plt.subplot(1 , 2 , 1)
plt.imshow(cells[0])
plt.title('Infected Cell')
plt.xticks([]) , plt.yticks([])
n = np.arange(cells.shape[0])
np.random.shuffle(n)
cells = cells[n]
labels = labels[n]
cells = cells.astype(np.float32)
labels = labels.astype(np.int32)
cells = cells/255
from sklearn.model_selection import train_test_split
train_x , x , train_y , y = train_test_split(cells , labels ,
test_size = 0.2 ,
random_state = 111)
eval_x , test_x , eval_y , test_y = train_test_split(x , y ,
test_size = 0.5 ,
random_state = 111)
plt.figure(1 , figsize = (15 ,5))
n = 0
for z , j in zip([train_y , eval_y , test_y] , ['train labels','eval labels','test labels']):
n += 1
plt.subplot(1 , 3 , n)
sns.countplot(x = z )
plt.title(j)
# plt.show()
print('train data shape {} ,eval data shape {} , test data shape {}'.format(train_x.shape,
eval_x.shape ,
test_x.shape))
from tensorflow.python.framework import ops
ops.reset_default_graph()
def cnn_model_fn(features , labels , mode):
input_layers = tf.reshape(features['x'] , [-1 , 50 , 50 ,3])
conv1 = tf.compat.v1.layers.Conv2D(
inputs = input_layers ,
filters = 50 ,
kernel_size = [7 , 7],
padding = 'same',
activation = tf.nn.relu
)
conv2 = tf.layers.conv2d(
inputs = conv1,
filters = 90,
kernel_size = [3 , 3],
padding = 'valid',
activation = tf.nn.relu
)
conv3 = tf.layers.conv2d(
inputs = conv2 ,
filters = 10,
kernel_size = [5 , 5],
padding = 'same',
activation = tf.nn.relu
)
pool1 = tf.layers.max_pooling2d(inputs = conv3 , pool_size = [2 , 2] ,
strides = 2 )
conv4 = tf.layers.conv2d(
inputs = pool1 ,
filters = 5,
kernel_size = [3 , 3],
padding = 'same',
activation = tf.nn.relu
)
pool2 = tf.layers.max_pooling2d(inputs = conv4 , pool_size = [2 , 2] ,
strides = 2 , padding = 'same')
pool2_flatten = tf.layers.flatten(pool2)
fc1 = tf.layers.dense(
inputs = pool2_flatten,
units = 2000,
activation = tf.nn.relu
)
fc2 = tf.layers.dense(
inputs = fc1,
units = 1000,
activation = tf.nn.relu
)
fc3 = tf.layers.dense(
inputs = fc2 ,
units = 500 ,
activation = tf.nn.relu
)
logits = tf.layers.dense(
inputs = fc3 ,
units = 2
)
predictions = {
'classes': tf.argmax(input = logits , axis = 1),
'probabilities': tf.nn.softmax(logits , name = 'softmax_tensor')
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode = mode ,
predictions = predictions)
loss = tf.losses.sparse_softmax_cross_entropy(labels = labels ,
logits = logits)
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.001)
train_op = optimizer.minimize(loss = loss ,
global_step = tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode = mode ,
loss = loss ,
train_op = train_op
)
eval_metric_op = {'accuracy' : tf.metrics.accuracy(labels = labels ,
predictions = predictions['classes'])}
logging_hook = tf.train.LoggingTensorHook(
tensors = tensors_to_log , every_n_iter = 50
)
return tf.estimator.EstimatorSpec(mode = mode ,
loss = loss ,
eval_metric_ops = eval_metric_op)
# Checkpoint saving training values
malaria_detector = tf.estimator.Estimator(model_fn = cnn_model_fn ,
model_dir = '/tmp/modelchkpt')
tensors_to_log = {'probabilities':'softmax_tensor'}
logging_hook = tf.estimator.LoggingTensorHook(
tensors = tensors_to_log , every_n_iter = 50
)
train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x = {'x': train_x},
y = train_y,
batch_size = 100 ,
num_epochs = None ,
shuffle = True
)
malaria_detector.train(input_fn = train_input_fn , steps = 1 , hooks = [logging_hook])
malaria_detector.train(input_fn = train_input_fn , steps = 10000)
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x = {'x': eval_x},
y = eval_y ,
num_epochs = 1 ,
shuffle = False
)
eval_results = malaria_detector.evaluate(input_fn = eval_input_fn)
print(eval_results)
pred_input_fn = tf.estimator.inputs.numpy_input_fn(
x = {'x' : test_x},
y = test_y,
num_epochs = 1,
shuffle = False
)
y_pred = malaria_detector.predict(input_fn = pred_input_fn)
classes = [p['classes'] for p in y_pred]
from sklearn.metrics import confusion_matrix , classification_report , accuracy_score
print('{} \n{} \n{}'.format(confusion_matrix(test_y , classes) ,
classification_report(test_y , classes) ,
accuracy_score(test_y , classes)))
plt.figure(1 , figsize = (15 , 9))
n = 0
for i in range(49):
n += 1
r = np.random.randint( 0 , test_x.shape[0] , 1)
plt.subplot(7 , 7 , n)
plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)
plt.imshow(test_x[r[0]])
plt.title('true {} : pred {}'.format(test_y[r[0]] , classes[r[0]]) )
plt.xticks([]) , plt.yticks([])
plt.show()
print("done")
这是错误：
File "CNN.py", line 240, in <module>
malaria_detector.train(input_fn = train_input_fn , steps = 1 , hooks = [logging_hook])
File "/usr/local/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 374, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1164, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1194, in _train_model_default
features, labels, ModeKeys.TRAIN, self.config)
File "/usr/local/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1152, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "CNN.py", line 136, in cnn_model_fn
activation = tf.nn.relu
File "/usr/local/lib/python2.7/site-packages/tensorflow_core/python/layers/convolutional.py", line 314, in __init__
name=name, **kwargs)
File "/usr/local/lib/python2.7/site-packages/tensorflow_core/python/keras/layers/convolutional.py", line 527, in __init__
**kwargs)
File "/usr/local/lib/python2.7/site-packages/tensorflow_core/python/keras/layers/convolutional.py", line 122, in __init__
**kwargs)
File "/usr/local/lib/python2.7/site-packages/tensorflow_core/python/layers/base.py", line 213, in __init__
**kwargs)
File "/usr/local/lib/python2.7/site-packages/tensorflow_core/python/training/tracking/base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py", line 186, in __init__
generic_utils.validate_kwargs(kwargs, allowed_kwargs)
File "/usr/local/lib/python2.7/site-packages/tensorflow_core/python/keras/utils/generic_utils.py", line 718, in validate_kwargs
raise TypeError(error_message, kwarg)
TypeError: ('Keyword argument not understood:', 'inputs')
我该如何解决TypeError？我已经安装了tensorflow 2.1并升级了keras。不知道这是否与该错误有关-这似乎是语法错误。
谢谢！-萨蒂亚
您询问（TypeError: ('Keyword argument not understood:', 'inputs')）的错误是由于conv2d在第一个卷积层中使用大写字母引起的。更改以下内容：
conv1 = tf.compat.v1.layers.Conv2D(
inputs = input_layers ,
filters = 50 ,
kernel_size = [7 , 7],
padding = 'same',
activation = tf.nn.relu
)
至：
conv1 = tf.compat.v1.layers.conv2d(
inputs = input_layers ,
filters = 50 ,
kernel_size = [7 , 7],
padding = 'same',
activation = tf.nn.relu
)
错误就会消失。
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