CODEWITHSUNDEEP
pythonkeras
pggsmiles
pggsmiles

Reputation: 21

Error when checking target: expected dense_2 to have shape (3,) but got array with shape (1,)

I changed the data type but I could not resolve the error.

I tried One-Hot Encoding but it doesn't work too.

I don't know what's wrong:(

seed = 0
np.random.seed(seed)
tf.set_random_seed(seed)

df = pd.read_csv('HW01_dataset_tae.txt', sep=',' ,header=None, names = ["Native", "Instructor", "Course", "Semester", "Class Size", "Evaluation"])

dataset = df.values # dataframe to int64
X = dataset[:,0:5] # attribute
Y_Eva = dataset[:,5] # class 

e = LabelEncoder()
e.fit(Y_Eva)
Y = e.transform(Y_Eva)

K = 10 
kFold = StratifiedKFold(n_splits=K, shuffle=True, random_state=seed) 

accuracy = []

for train_index, test_index in kFold.split(X,Y):
    model = Sequential() 
    model.add(Dense(16, input_dim=5, activation='relu')) 
    model.add(Dense(10, activation='relu')) 
    model.add(Dense(3, activation='softmax'))
    model.compile(loss='mean_squared_error', 
              optimizer='adam', 
              metrics=['accuracy'])
    model.fit(X[train_index], Y[train_index], epochs=100, batch_size=2)

the error ; Error when checking target: expected dense_2 to have shape (3,) but got array with shape (1,)

is detected at here ; model.fit(X[train_index], Y[train_index], epochs=100, batch_size=2).

What shout I do?

Upvotes: 1

Views: 259

Answers (2)

pggsmiles
pggsmiles

Reputation: 21

I solved the problem.

At this code,

model.fit(X[train_index], Y[train_index], epochs=100, batch_size=2)

the number of rows in 'Y[train_index]' must be three because the classes are three.

The error came out since each Y[train_index] has only one row.

So, I used One-Hot Encoding and changed the code like this.

e = LabelEncoder()
e.fit(Y_Eva)
Y = e.transform(Y_Eva)
Y_encoded = np_utils.to_categorical(Y) # changed code

K = 10 
kFold = StratifiedKFold(n_splits=K, shuffle=True, random_state=seed) 

accuracy = []

for train_index, test_index in kFold.split(X,Y):
    model = Sequential() 
    model.add(Dense(32, input_dim=5, activation='relu')) 
    model.add(Dense(16, activation='relu')) 
    model.add(Dense(3, activation='softmax'))
    model.compile(loss='categorical_crossentropy', 
              optimizer='adam', 
              metrics=['accuracy'])
    model.fit(X[train_index], Y_encoded[train_index], epochs=100, batch_size=2) # changed code

Finally, I was able to run the code.

Upvotes: 1

Marius Johan
Marius Johan

Reputation: 392

TensorFlow has made some documenation on the dense layer, and if you then instead of saying input_dim says input_shape you can specify the prefered shape.
https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense

model = Sequential()
model.add(Dense(16, input_shape=(5,)))  # Then your data has to be of shape (batch x 5)

When you then are adding another dense layer, you actaully don't have to provide the input_sahpe

model.add(Dense(10))

Upvotes: 0

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