keras, Conv2DでのValueErrorの解決法がわからない

ValueError: Input 0 of layer "conv2d" is incompatible with the layer: expected min_ndim=4, found ndim=1. Full shape received: (None,)
というエラーが出たのですがどうしたらエラーが出なくなるのかわかりません．
model.add(Conv2D(32,(3,3), padding='same',input_shape=X.shape[1:]))
にエラーが出ているようで，読み込んだデータがうまく変換されていないようなのですがどこを変更すればいいのかわかりません．．．
エラーの対処法や解決法を教えてください！イメージ説明

Python
!pip install icrawler

from icrawler.builtin import BingImageCrawler

# 猫の画像を取得
crawler = BingImageCrawler(storage={"root_dir": "cat"})
crawler.crawl(keyword="猫", max_num=10)

from icrawler.builtin import BingImageCrawler

# 猫の画像を取得
crawler = BingImageCrawler(storage={"root_dir": "dog"})
crawler.crawl(keyword="犬", max_num=10)

from PIL import Image
import os, glob
import numpy as np
from PIL import ImageFile
# IOError: image file is truncated (0 bytes not processed)回避のため
ImageFile.LOAD_TRUNCATED_IMAGES = True

classes = ["dog", "cat"]
num_classes = len(classes)
image_size = 64
num_testdata = 25

X_train = []
X_test  = []
y_train = []
y_test  = []

for index, classlabel in enumerate(classes):
    photos_dir = "./" + classlabel
    files = glob.glob(photos_dir + "/*.jpg")
    for i, file in enumerate(files):
        image = Image.open(file)
        image = image.convert("RGB")
        image = image.resize((image_size, image_size))
        data = np.asarray(image)
        if i < num_testdata:
            X_test.append(data)
            y_test.append(index)
        else:

            # angleに代入される値
            # -20
            # -15
            # -10
            #  -5
            # 0
            # 5
            # 10
            # 15
            for angle in range(-20, 20, 5):

                img_r = image.rotate(angle)
                data = np.asarray(img_r)
                X_train.append(data)
                y_train.append(index)
                # FLIP_LEFT_RIGHT　は 左右反転
                img_trains = img_r.transpose(Image.FLIP_LEFT_RIGHT)
                data = np.asarray(img_trains)
                X_train.append(data)
                y_train.append(index)

X_train = np.array(X_train)
X_test  = np.array(X_test)
y_train = np.array(y_train)
y_test  = np.array(y_test)

xy = (X_train, X_test, y_train, y_test)
np.save("./dog_cat.npy", xy)

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.utils import np_utils
from tensorflow import keras
import numpy as np

classes = ["dog", "cat"]
num_classes = len(classes)
image_size = 64

"""
データを読み込む関数
"""
def load_data():
    X_train, X_test, y_train, y_test = np.load("./dog_cat.npy", allow_pickle=True)
    # 入力データの各画素値を0-1の範囲で正規化(学習コストを下げるため)
    X_train = X_train.astype("float") / 255
    X_test  = X_test.astype("float") / 255
    # to_categorical()にてラベルをone hot vector化
    y_train = np_utils.to_categorical(y_train, num_classes)
    y_test  = np_utils.to_categorical(y_test, num_classes)

    return X_train, y_train, X_test, y_test

"""
モデルを学習する関数
"""
def train(X, y, X_test, y_test):
    model = Sequential()

    # Xは(1200, 64, 64, 3)
    # X.shape[1:]とすることで、(64, 64, 3)となり、入力にすることが可能です。
    model.add(Conv2D(32,(3,3), padding='same',input_shape=X.shape[1:]))
    model.add(Activation('relu'))
    model.add(Conv2D(32,(3,3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Dropout(0.1))

    model.add(Conv2D(64,(3,3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(64,(3,3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.45))
    model.add(Dense(2))
    model.add(Activation('softmax'))

    # https://keras.io/ja/optimizers/
    # 今回は、最適化アルゴリズムにRMSpropを利用
    opt = keras.optimizers.rmsprop(lr=0.00005, decay=1e-6)
    # https://keras.io/ja/models/sequential/
    model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])
    model.fit(X, y, batch_size=28, epochs=40)
    # HDF5ファイルにKerasのモデルを保存
    model.save('./cnn.h5')

    return model

"""
メイン関数
データの読み込みとモデルの学習を行います。
"""
def main():
    # データの読み込み
    X_train, y_train, X_test, y_test = load_data()

    # モデルの学習
    model = train(X_train, y_train, X_test, y_test)

main()