How to fit my data into my model using keras

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Python3.6

Keras

I have a list of images with shape (150 x 150 x 3) the list of images contains 100 images and array of labels, a label for each image with total count 100 label.
And I have the label of each image in an array
Now I want to fit my data in that model I tried model.fit(x,y) but it didn't work it gave me this error
```

ValueError: Data cardinality is ambiguous:
  x sizes: 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 113, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 110, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150
  y sizes: 100
Make sure all arrays contain the same number of samples.

```

What I have tried:

this is how i read and store my images

Trainimages=[]
    Testimages = []
    imagepaths=list(paths.list_images("C:/Users/Geforce/Downloads/Compressed/ci-sc22-places-and-scene-recognition/train_images"))
    imagepathstest=list(paths.list_images("C:/Users/Geforce/Downloads/Compressed/ci-sc22-places-and-scene-recognition/test_images"))
    print(imagepaths[0])
    img=cv2.imread(imagepaths[0])
    
    for i in range(100):
        im = cv2.imread(imagepaths[i])
        Trainimages.append(im)
      x=Trainimages

this is how i set the labels from the .csv file

df = pd.read_csv('train2.csv')
    print(df["image_name"])
    n = copy.deepcopy(df["label"])
    print("---------------------")
    imgname = df["image_name"]
    for i in range(len(imgname)):
        # print(imgname.iloc[i])
        x = imgname.iloc[i].split(".")
        n[i] = int(x[0])

    df["X"] = n
    print(df)
    df = df.sort_values(by="X")
    df = df.drop("X", 1)
    print("--------------------------------------")
    print(df)

    
   
    print(type(g))
    y= df["labels"].to_numpy()

this is my model

def myModel():
    input=Input((1,150,150,3))
    out=Conv2D(filters=96,kernel_size=(11,11),strides=(1,1),name="conv1")(input)
    out=Activation('relu')(out)
    out = Conv2D(filters=256, kernel_size=(5, 5), strides=(1, 1), name="conv2")(out)
    out = Activation('relu')(out)
    out = Conv2D(filters=384, kernel_size=(3, 3), strides=(1, 1), name="conv3")(out)
    out = Activation('relu')(out)
    out = Conv2D(filters=384, kernel_size=(3, 3), strides=(1, 1), name="conv4")(out)
    out = Activation('relu')(out)
    out = Conv2D(filters=256, kernel_size=(3, 3), strides=(1, 1), name="conv5")(out)
    out = Activation('relu')(out)
    out=  Dense(units=4096,activation='relu',name="FC1")(out)
    out = Dense(units=4096, activation='relu', name="FC2")(out)
    out = Dense(units=6, activation='softmax', name="classes")(out)
    model=keras.models.Model(inputs=input,outputs=out)
    model.compile(loss="sparse_categorical_crossentropy",optimizer=Adam(lr=0.01))
    return model

this is how i fit my data
```
model.fit(x,y)
```