Question

我在Keras中拥有以下内容：

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('model.h5')

我尝试在一组图像上测试模型，如下所示：

for root, dirs, files in os.walk(test_directory):
    for file in files:
        img = cv2.imread(root + '/' + file)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        img = cv2.resize(img, (512,512))
        img_class = model.predict_classes(img)
        prediction = img_class[0]

但是我得到了：

ValueError: Error when checking : expected dense_1_input to have shape (None, 131072) but got array with shape (512, 512)

在线：

img_class = model.predict_classes(img)

那是为什么？

谢谢。

如何在一组图像上测试Keras模型？

0 个答案: