ValueError: Ошибка при проверке: ожидается, что dens_1_input имеет 2 измерения, но получил массив с формой (1, 16, 16, 512)

Question

У меня следующая ошибка:

ValueError: Ошибка при проверке: ожидается, что плотность_данных_1 равна 2 размеры, но получили массив с формой (1, 16, 16, 512)

, который встречается в этой строке:

img_class = model.predict_classes (feature_value)

Есть идеи, как решить проблему?

Это полный скрипт:

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]

Спасибо.

Answer 1

Вы пытаетесь предсказать, используя массив 4D:

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

Но вы обучили сеть на двумерном массиве:

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

Вы должны предсказывать, используя те же фигуры, на которых вы тренировали модель:

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