kerasで予測の段階でラベルを返す方法について

caifar-10でcnnで学習が終わった状態から、画像を読み込ませ、予測ラベルを返したいです。画像の形式はtfrecord形式で返し、[128,32,32,3]です

ところが問題あり、質問があります

質問
１、InceptionResNetV2を組んだのですが、通常出力形式は(batch_size, class数)
の形になるはずだと思ってます。しかしmodel.predictで返ってきたのは(2048,10)の画像でした。なぜこのような形式になるのでしょうか？

２,クラスを返したいのに、model.predict_classes()は使えなくなっていました。
このサイトのようにargmaxでやってもラベルが返りません。ラベルを返すのはどうしたら良いのでしょうか？

アドバイスや考えなどなんでもいいのでご教授お願いいたします。

重みを読み込むんでから
画像の出力はこちらサイト(訓練画像用)のとこちらのサイト関数(test画像用)を使ってラベルをone-hotで出力してます

sess = K.get_session()

test_image, test_labels = inputs('/Users/tatsuyahagiwara/Downloads/test.tfrecords',128)

test_model_input = layers.Input(tensor=test_image)
test_model_output = InceptionResNetV2(img_input=test_model_input)
test_model = Model(inputs=test_model_input, outputs=test_model_output, name='inception_resnet_v2')
test_model.load_weights('/Users/tatsuyahagiwara/Downloads/check_point_dir/model.ep01.h5')


test_model.compile(optimizer=SGD(decay=1e-6, momentum=0.9, nesterov=True),
                        loss='categorical_crossentropy',
                    metrics=['accuracy'], target_tensors=[test_labels])

coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess, coord)
y_pred = test_model.predict(x=None,batch_size=None,verbose=0,steps=16)
print(y_pred)
coord.request_stop()
coord.join(threads)

y_pred.shape
>>>(2048, 10)

InceptionResNetV2本体

def conv2d_bn(x, filters, kernel_size, strides=1,
              padding='same', activation='relu', use_bias=None, name=None):
    x = Conv2D(filters, kernel_size, strides=strides, padding=padding,
               use_bias=use_bias, name=name)(x)
    if not use_bias:
        bn_axis = 1 if K.image_data_format() == 'channels_first' else 3
        bn_name = None if name is None else name + '_bn'
        x = BatchNormalization(axis=bn_axis, scale=True, name=bn_name)(x)
    if activation is not None:
        ac_name = None if name is None else name + '_ac'
        x = Activation(activation, name=ac_name)(x)
    return x


def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'):

    if block_type == 'block35':
        branch_0 = conv2d_bn(x, 32, 1)
        branch_1 = conv2d_bn(x, 32, 1)
        branch_1 = conv2d_bn(branch_1, 32, 3)
        branch_2 = conv2d_bn(x, 32, 1)
        branch_2 = conv2d_bn(branch_2, 48, 3)
        branch_2 = conv2d_bn(branch_2, 64, 3)
        branches = [branch_0, branch_1, branch_2]
    elif block_type == 'block17':
        branch_0 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(x, 128, 1)
        branch_1 = conv2d_bn(branch_1, 160, [1, 7])
        branch_1 = conv2d_bn(branch_1, 192, [7, 1])
        branches = [branch_0, branch_1]
    elif block_type == 'block8':
        branch_0 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(x, 192, 1)
        branch_1 = conv2d_bn(branch_1, 224, [1, 3])
        branch_1 = conv2d_bn(branch_1, 256, [3, 1])
        branches = [branch_0, branch_1]
    else:
        raise ValueError('Unknown Inception-ResNet block type. '
                         'Expects "block35", "block17" or "block8", '
                         'but got: ' + str(block_type))

    block_name = block_type + '_' + str(block_idx)
    channel_axis = 1 if K.image_data_format() == 'channels_first' else 3
    mixed = Concatenate(axis=channel_axis, name=block_name + '_mixed')(branches)
    up = conv2d_bn(mixed, K.int_shape(x)[channel_axis],1, activation=None,
                   use_bias=True, name=block_name + '_conv')

    x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,
               output_shape=K.int_shape(x)[1:],
               arguments={'scale': scale},
               name=block_name)([x, up])
    if activation is not None:
        x = Activation(activation, name=block_name + '_ac')(x)
    return x



def InceptionResNetV2(img_input, weights=None,
                      pooling=None, classes=10):

    # Stem block: 35 x 35 x 192
    x = conv2d_bn(img_input, 32, 3, strides=2, padding='same')
    x = conv2d_bn(x, 32, 3, padding='same')
    x = conv2d_bn(x, 64, 3)
    x = MaxPooling2D(3, strides=2)(x)
    x = conv2d_bn(x, 80, 1, padding='same')
    x = conv2d_bn(x, 192, 3, padding='same')
    x = MaxPooling2D(3, strides=2)(x)

    # Mixed 5b (Inception-A block): 35 x 35 x 320
    branch_0 = conv2d_bn(x, 96, 1)
    branch_1 = conv2d_bn(x, 48, 1)
    branch_1 = conv2d_bn(branch_1, 64, 5)
    branch_2 = conv2d_bn(x, 64, 1)
    branch_2 = conv2d_bn(branch_2, 96, 3)
    branch_2 = conv2d_bn(branch_2, 96, 3)
    branch_pool = AveragePooling2D(3, strides=1, padding='same')(x)
    branch_pool = conv2d_bn(branch_pool, 64, 1)
    branches = [branch_0, branch_1, branch_2, branch_pool]
    channel_axis = 1 if K.image_data_format() == 'channels_first' else 3
    x = Concatenate(axis=channel_axis, name='mixed_5b')(branches)

    # 10x block35 (Inception-ResNet-A block): 35 x 35 x 320
    for block_idx in range(1, 11):
        x = inception_resnet_block(x,
                                   scale=0.17,
                                   block_type='block35',
                                   block_idx=block_idx)

    # Mixed 6a (Reduction-A block): 17 x 17 x 1088
    branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='same')
    branch_1 = conv2d_bn(x, 256, 1)
    branch_1 = conv2d_bn(branch_1, 256, 3)
    branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='same')
    branch_pool = MaxPooling2D(3, strides=2, padding='same')(x)
    branches = [branch_0, branch_1, branch_pool]
    x = Concatenate(axis=channel_axis, name='mixed_6a')(branches)

    # 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088
    for block_idx in range(1, 21):
        x = inception_resnet_block(x,
                                   scale=0.1,
                                   block_type='block17',
                                   block_idx=block_idx)

    # Mixed 7a (Reduction-B block): 8 x 8 x 2080
    branch_0 = conv2d_bn(x, 256, 1)
    branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='same')
    branch_1 = conv2d_bn(x, 256, 1)
    branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='same')
    branch_2 = conv2d_bn(x, 256, 1)
    branch_2 = conv2d_bn(branch_2, 288, 3)
    branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='same')
    branch_pool = MaxPooling2D(3, strides=2, padding='same')(x)
    branches = [branch_0, branch_1, branch_2, branch_pool]
    x = Concatenate(axis=channel_axis, name='mixed_7a')(branches)

    # 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080
    for block_idx in range(1, 10):
        x = inception_resnet_block(x,
                                   scale=0.2,
                                   block_type='block8',
                                   block_idx=block_idx)
    x = inception_resnet_block(x,
                               scale=1.,
                               activation=None,
                               block_type='block8',
                               block_idx=10)

    # Final convolution block: 8 x 8 x 1536
    x = conv2d_bn(x, 1536, 1, name='conv_7b')

    
    # Classification block
    x = GlobalAveragePooling2D(name='avg_pool')(x)
    return Dense(classes, activation='softmax', name='predictions')(x)