こちらを参考に自信で準備したイメージを基に,
2つのクラスを持つ学習モデルの実装を試みましたが, ValueErrorが出てしまいます.

実装環境
・Anaconda 4.3.14
・Python 3.5.3
・tensorflow 1.2.0rc2
・keras 2.0.4
・numpy 1.12.1

predict_generator() に問題があるのだと思いますが, 調べても解決できない状況にあり, 投稿させていただきました.

以下に詳細を記します.

実装したコード

# -*- coding: utf-8 -*-
from __future__ import print_function
import os
import sys
import h5py
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
from keras.applications import VGG16


img_w = 150
img_h = 150

top_model_weights_path = 'bottleneck_fc_model.h5'
train_data_dir = 'data/train'
validation_data_dir = 'data/validation'

# classes = ['landscape', 'portrait']
# classes = ['landscape', 'portraits']
# nb_classes = len(classes)

nb_train_samples = 180
nb_validation_samples = 20
# nb_samples_per_class = 90
epochs = 10
batch_size = 16


# VGG16 modelのFC層直前の特徴量を保存する
def save_bottleneck_features():
    datagen = ImageDataGenerator(
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest')

    # VGG16ネットワークを作成
    model = VGG16(include_top=False, weights='imagenet')

    generator = datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_w, img_h),
        batch_size=batch_size,
        shuffle=False,
        class_mode=None)
        # classes=classes)
    bottleneck_features_train = model.predict_generator(
        generator, nb_train_samples // batch_size)
    np.save(open('bottleneck_features_train.npy', 'wb'),
            bottleneck_features_train)

    generator = datagen.flow_from_directory(
        validation_data_dir,
        target_size=(img_w, img_h),
        batch_size=batch_size,
        shuffle=False,
        class_mode=None)
        # classes=classes)
    bottleneck_features_validation = model.predict_generator(
        generator, nb_validation_samples // batch_size)
    np.save(open('bottleneck_features_validation.npy', 'wb'),
            bottleneck_features_validation)


# save_bottleneck_featuresで保存した特徴量を基にFC層を作成して学習させる
def train_top_model():
    train_data = np.load(open('bottleneck_features_train.npy', 'rb'))
    train_labels = np.array([0] * int(nb_train_samples / 2) + [1] * int(nb_train_samples / 2))

    validation_data = np.load(open('bottleneck_features_validation.npy', 'rb'))
    validation_labels = np.array([0] * int(nb_validation_samples / 2) + [1] * int(nb_validation_samples / 2))

    # (180, 4, 4, 512) 期待する値
    print(train_data.shape)
    # (20, 4, 4, 512)　期待する値
    print(validation_data.shape)

    # FCネットワークを作成
    top_model = Sequential()
    top_model.add(Flatten(input_shape=train_data.shape[1:]))
    top_model.add(Dense(256, activation='relu'))
    top_model.add(Dropout(0.5))
    top_model.add(Dense(1, activation='sigmoid'))

    top_model.compile(
        optimizer='rmsprop',
        loss='binary_crossentropy',
        metrics=['accuracy'])

    # visualize model
    top_model.summary()
    from keras.utils import plot_model
    plot_model(top_model, to_file="model.png", show_shapes=True)


    # 学習
    top_model.fit(
        train_data,
        train_labels,
        epochs=epochs,
        batch_size=batch_size,
        validation_data=(validation_data, validation_labels))
        # validation_split=0.05)

    # 学習した重みを保存
    top_model.save_weights(top_model_weights_path)


save_bottleneck_features()
train_top_model()

以下がエラーの詳細になります.

Found 180 images belonging to 2 classes.
Found 20 images belonging to 2 classes.
(176, 4, 4, 512) #print(train_data.shape)の値
(16, 4, 4, 512) #print(validation_data.shape)の値
Traceback (most recent call last):
  File "train.py", line 115, in <module>
    train_top_model()
  File "train.py", line 107, in train_top_model
    validation_data=(validation_data, validation_labels))
  File "C:\Users\username\Anaconda3\envs\py35\lib\site-packages\keras\models.py", line 856, in fit
    initial_epoch=initial_epoch)
  File "C:\Users\username\Anaconda3\envs\py35\lib\site-packages\keras\engine\training.py", line 1429, in fit
    batch_size=batch_size)
  File "C:\Users\username\Anaconda3\envs\py35\lib\site-packages\keras\engine\training.py", line 1317, in _standardize_user_data
    _check_array_lengths(x, y, sample_weights)
  File "C:\Users\username\Anaconda3\envs\py35\lib\site-packages\keras\engine\training.py", line 235, in _check_array_lengths
    'and ' + str(list(set_y)[0]) + ' target samples.')
ValueError: Input arrays should have the same number of samples as target arrays. Found 176 input samples and 180 target samples.