CODEWITHSUNDEEP
pythontensorflowkeras
Alessandro Bossi
Alessandro Bossi

Reputation: 9

Tensorflow I don't understand what should be the input of model.fit

I am a beginner and I am trying to classify some images in 20 classes

This is the code I am trying to use:

from tensorflow.python.keras import Sequential
from tensorflow.python.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
x=x/255.0
model=Sequential()
model.add(Conv2D(64,(3,3), input_shape=x.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64,(3,3), input_shape=x.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(64))

model.add(Dense(20))
model.add(Activation('softmax'))




loss = tf.keras.losses.CategoricalCrossentropy()

lr = 1e-3
optimizer = tf.keras.optimizers.Adam(learning_rate=lr)

metrics = ['accuracy']

model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
Y= np.asarray(y)

model.fit(x,Y,batch_size=32,validation_split=0.1)

But i receive this error:

ValueError: You are passing a target array of shape (1554, 1) while using as loss `categorical_crossentropy`. `categorical_crossentropy` expects targets to be binary matrices (1s and 0s) of shape (samples, classes). If your targets are integer classes, you can convert them to the expected format via:

x.shape returns (1554, 50, 50, 1)

and Y.shape returns

Thanks for the help! (1554,)

Upvotes: 0

Views: 45

Answers (1)

Kazesui
Kazesui

Reputation: 159

You probably forgot to one-hot encode your label data. You can use to_categorical in keras.utils to convert your labels accordingly if they're integers from 0 to 19 (representing the different classes)

Upvotes: 1

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