Keras multi-label classification: Failed to convert a NumPy array to a Tensor (Unsupported object type int)

Question

I am trying to do multi-label classification using Keras. I got my dataset from kaggle.

Link to dataset: https://www.kaggle.com/dadajonjurakuziev/movieposter

I am getting an error when I am trying to fit the model.

THE CODE:

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=20, test_size=0.3)


model = Sequential()

model.add(Conv2D(filters=16, kernel_size=(5, 5), activation="relu", input_shape=(SIZE,SIZE,3)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(Conv2D(filters=32, kernel_size=(5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Dropout(0.2))

model.add(Conv2D(filters=64, kernel_size=(5, 5), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Dropout(0.2))

model.add(Conv2D(filters=64, kernel_size=(5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())
model.add(Dropout(0.2))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(25, activation='sigmoid'))

model.summary()

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
#print(X_train, y_train, X_test, y_test)
history = model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test)) #This is the line where I am getting an error

THE ERROR:

ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type int).

Here are some values of X_train and y_train:

X_train[0]:

array([[[0.15294118, 0.1764706 , 0.3019608 ],
        [0.15294118, 0.1764706 , 0.3019608 ],
        [0.15686275, 0.18039216, 0.30588236],
        ...,
        [0.69803923, 0.69411767, 0.75686276],
        [0.69803923, 0.69411767, 0.75686276],
        [0.69803923, 0.69411767, 0.75686276]],

       [[0.11764706, 0.14509805, 0.25882354],
        [0.11764706, 0.14509805, 0.25882354],
        [0.12156863, 0.14509805, 0.27058825],
        ...,
        [0.69411767, 0.6901961 , 0.7529412 ],
        [0.69411767, 0.6901961 , 0.7529412 ],
        [0.69411767, 0.6901961 , 0.7529412 ]],

       [[0.10588235, 0.13333334, 0.24313726],
        [0.10588235, 0.13333334, 0.24313726],
        [0.10980392, 0.13725491, 0.2509804 ],
        ...,
        [0.6901961 , 0.6862745 , 0.7490196 ],
        [0.6901961 , 0.6862745 , 0.7490196 ],
        [0.6901961 , 0.6862745 , 0.7490196 ]],

       ...,

       [[0.16862746, 0.30588236, 0.62352943],
        [0.16862746, 0.30588236, 0.62352943],
        [0.16862746, 0.30588236, 0.62352943],
        ...,
        [0.16862746, 0.30588236, 0.62352943],
        [0.16862746, 0.30588236, 0.62352943],
        [0.16862746, 0.30588236, 0.62352943]],

       [[0.16862746, 0.30588236, 0.62352943],
        [0.16862746, 0.30588236, 0.62352943],
        [0.16862746, 0.30588236, 0.62352943],
        ...,
        [0.16862746, 0.30588236, 0.62352943],
        [0.16862746, 0.30588236, 0.62352943],
        [0.16862746, 0.30588236, 0.62352943]],

       [[0.1764706 , 0.31764707, 0.62352943],
        [0.1764706 , 0.31764707, 0.62352943],
        [0.1764706 , 0.31764707, 0.62352943],
        ...,
        [0.1764706 , 0.31764707, 0.62352943],
        [0.1764706 , 0.31764707, 0.62352943],
        [0.1764706 , 0.31764707, 0.62352943]]], dtype=float32)

y_train[0]:

array(['https://m.media-amazon.com/images/M/MV5BY2IyODNmMjctMjNlNi00NmU0LTkxYTEtMTBjNzJiY2U0ZTI2XkEyXkFqcGdeQXVyMjI3MTc2MzU@._V1_UX256,0,256,256_AL_.jpg',
       'The Lawyer', 2020, 7.1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],
      dtype=object)

I am not sure what the object dtype is. I am pretty new to these topics. Could someone please help me? Thanks!

It sounds like your numpy arrays are of type int while keras expects floats. Try X = X.astype('float32') and Y = Y.astype('float32') before doing anything else with them. — Lukas S
– Lukas S, Commented Jul 10, 2021 at 10:41

LGrementieri · Accepted Answer · 2021-07-10 15:55:44Z

0

The error is the same of the question Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray) on ImageDataGenerator in Keras.

The function model.fit expects X_train and y_train to be np.ndarrays of numeric types like np.float32 or np.int32. Here, instead the type of y_train is np.object because this array contains elements with mixed types (strings and integers).

Probably you should process the labels in y_train and remove the strings, because you cannot use strings to compute a loss.

answered Jul 10, 2021 at 15:55

LGrementieri

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