ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape

[glushakviktor]

Hello, can you help me please. When I try to check my result on my own image. I have an error.
My code same as yours from listings 5.17 to 5.19:

import os
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.applications import VGG16

conv_base = VGG16(weights='imagenet', include_top=False, input_shape=(150,150,3))

base_dir = 'new_docs/'
train_dir = os.path.join(base_dir,'train')
validation_dir = os.path.join(base_dir,'validation')
test_dir = os.path.join(base_dir,'test')

datagen = ImageDataGenerator(rescale=1./255)
batch_size = 20

def extract_features(directory, sample_count):
    features = np.zeros(shape=(sample_count, 4, 4, 512))
    labels = np.zeros(shape=(sample_count))

    generator = datagen.flow_from_directory(
        directory,
        target_size=(150,150),
        batch_size=batch_size,
        class_mode='binary'
    )
    i = 0
    for input_batch, labels_batch in generator:
        features_batch = conv_base.predict(input_batch)
        features[i * batch_size : (i + 1) * batch_size] = features_batch
        labels[i * batch_size : (i + 1) * batch_size] = labels_batch
        i += 1
        if i * batch_size >= sample_count:
            break
        return features, labels

train_features, train_labels = extract_features(train_dir, 2000)
validation_features, validation_labels = extract_features(validation_dir, 1000)
test_features, test_labels = extract_features(test_dir, 1000)

train_features = np.reshape(train_features, (2000, 4 * 4 * 512))
validation_features = np.reshape(validation_features, (1000, 4 * 4 * 512))
test_features = np.reshape(test_features, (1000, 4 * 4 * 512))

from keras import models
from keras import layers
from keras import optimizers

model = models.Sequential()
model.add(layers.Dense(256, activation='relu', input_dim= 4 * 4 * 512))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(
    optimizer=optimizers.RMSprop(lr=2e-5),
    loss='binary_crossentropy',
    metrics=['acc']
)

history = model.fit(train_features, train_labels,
                    epochs=30,
                    batch_size=20,
                    validation_data=(validation_features, validation_labels))

model.save('pas_vgg16_2.h5')

Then after I saved a model, and in another file I loaded it

from keras.models import load_model
from keras.preprocessing import image
import numpy as np

model = load_model('pas_vgg16_2.h5')
model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

img_width, img_height = 150, 150
batch_size = 20
image_src = 'test.jpg'

# predicting images
img = image.load_img(image_src, target_size=(img_width, img_height))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)

images = np.vstack([x])
classes = model.predict_classes(images, batch_size=batch_size)
print(classes)

Then I have the error:

ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 8192 but received input with shape (None, 150, 150, 3)