ValueError：检查时出错：预期density_1_input具有2维，但数组的形状为（1，16，16，512）

Question

我遇到以下错误：

ValueError：检查时出错：预期density_1_input具有2维，但数组的形状为（1，16，16，512）

发生在这一行：

img_class = model.predict_classes（feature_value）

关于如何解决此问题的任何想法吗？

这是完整的脚本：

from keras.applications import VGG16
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions
from keras.layers import Input, Flatten, Dense
from keras.models import Model
from keras import models
from keras import layers
from keras import optimizers
import ssl
import os
import cv2
import numpy as np
import matplotlib
# Force matplotlib to not use any Xwindows backend
matplotlib.use('Agg')
import matplotlib.pyplot as plt

# path to the training, validation, and testing directories

train_directory = '/train'
validation_directory = '/valid'
test_directory = '/test'
results_directory = '/results'
number_of_training_samples = 1746
number_of_validation_samples = 108
number_of_test_samples = 510
batch_size = 20

ssl._create_default_https_context = ssl._create_unverified_context

# get back the convolutional part of a VGG network trained on ImageNet
conv_base = VGG16(weights='imagenet',include_top=False,input_shape=(512,512,3))
conv_base.summary() 

# preprocess the data

# rescale images by the factor 1/255
train_data = ImageDataGenerator(rescale=1.0/255)
validation_data = ImageDataGenerator(rescale=1.0/255)
test_data = ImageDataGenerator(rescale=1.0/255)

train_features = np.zeros(shape=(number_of_training_samples,16,16,512))
train_labels = np.zeros(shape=(number_of_training_samples))

train_generator = train_data.flow_from_directory(
    train_directory,
    target_size=(512,512),
    batch_size=batch_size,
    class_mode='binary',
    shuffle=True)

i = 0
for inputs_batch, labels_batch in train_generator:
    features_batch = conv_base.predict(inputs_batch)
    train_features[i*batch_size:(i+1)*batch_size] = features_batch
    train_labels[i*batch_size:(i+1)*batch_size] = labels_batch
    i += 1
    if i * batch_size >= number_of_training_samples:
        break

train_features = np.reshape(train_features, (number_of_training_samples,16*16*512))

validation_features = np.zeros(shape=(number_of_validation_samples,16,16,512))
validation_labels = np.zeros(shape=(number_of_validation_samples))

validation_generator = validation_data.flow_from_directory(
    validation_directory,
    target_size=(512,512),
    batch_size=batch_size,
    class_mode='binary',
    shuffle=False)

i = 0
for inputs_batch, labels_batch in validation_generator:
    features_batch = conv_base.predict(inputs_batch)
    validation_features[i*batch_size:(i+1)*batch_size] = features_batch
    validation_labels[i*batch_size:(i+1)*batch_size] = labels_batch
    i += 1
    if i * batch_size >= number_of_validation_samples:
        break

validation_features = np.reshape(validation_features, (number_of_validation_samples,16*16*512))

test_generator = test_data.flow_from_directory(
    test_directory,
    target_size=(512,512),
    batch_size=batch_size,
    class_mode='binary',
    shuffle=False)

# define the Convolutional Neural Network (CNN) model
model = models.Sequential() 
model.add(layers.Dense(1024,activation='relu',input_dim=16*16*512))
model.add(layers.Dense(1,activation='sigmoid'))

# compile the model

model.compile(loss='binary_crossentropy',
    optimizer=optimizers.Adam(lr=0.01),
    metrics=['acc'])

# fit the model to the data
history = model.fit(train_features,
    train_labels,
    epochs=1,
    batch_size=batch_size,
    validation_data=(validation_features,validation_labels))

# save the model
model.save('benign_and_melanoma_from_scratch.h5')

# generate accuracy and loss curves for the training process (history of accuracy and loss)
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

number_of_epochs = range(1,len(acc)+1)

plt.plot(number_of_epochs, acc, 'r', label='Training accuracy')
plt.plot(number_of_epochs, val_acc, 'g', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend()
plt.savefig('accuracy.png')

plt.close()

plt.plot(number_of_epochs, loss, 'r', label='Training loss')
plt.plot(number_of_epochs, val_loss, 'g', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.savefig('loss.png')

# evaluate the model

# predict classes
for root, dirs, files in os.walk(test_directory):
    for file in files:
        img = cv2.imread(root + '/' + file)
        img = cv2.resize(img,(512,512),interpolation=cv2.INTER_AREA)
        img = np.expand_dims(img, axis=0)
        img = img/255.0
        feature_value = conv_base.predict(img)
        feature_value= np.reshape(feature_value,(1,16,16,512))
        img_class = model.predict_classes(feature_value)
        prediction = img_class[0]

谢谢。

Answer 1

您正在尝试使用4D数组进行预测：

feature_value= np.reshape(feature_value,(1,16,16,512))

但是您在2D阵列上训练了网络：

train_features = np.reshape(train_features, (number_of_training_samples,16*16*512))

您应该使用对模型进行训练时使用的相同形状进行预测：

feature_value= np.reshape(feature_value,(1,16*16*512))