Problem with the code

[Diego430]

[3 1 1 0 1 2 1 3 3 0 1 2 2 1 1 1 1 3 2 0 3 0 1 1 1 1 1 2 1 3 1 0 1 3 0 0 3
3 1 0 1 0 1 2 0 3 3 3 0 0 3 1 1 1 0 1 0 3 3 0 3 3 3 1 2 0 0 0]
(0, 0, 0, 0, 1, 0, 0, 3, 3, 0, 2, 0, 2, 0, 0, 0, 2, 3, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 3, 3, 0, 0, 0, 3, 0, 0, 3, 3, 1, 0, 0, 0, 1, 0, 0, 0, 3, 3, 0, 0, 0, 1, 2, 1, 0, 1, 0, 3, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0)
Accuracy:0.574

Confusion matrix: [[18 0 0 0]
[14 7 3 1]
[ 6 0 1 0]
[ 5 0 0 13]]
Traceback (most recent call last):
File "emotionRecognition_cnn.py", line 63, in
cnn_example()
File "emotionRecognition_cnn.py", line 33, in cnn_example
predicted = cnn.predict_one(feature)
File "/home/diego/Desktop/progetto/cnn_emotionrecognition/dnn.py", line 98, in predict_one
return np.argmax(self.model.predict(np.array([sample])))
File "/home/diego/.local/lib/python3.7/site-packages/keras/engine/training.py", line 1149, in predict
x, _, _ = self._standardize_user_data(x)
File "/home/diego/.local/lib/python3.7/site-packages/keras/engine/training.py", line 751, in _standardize_user_data
exception_prefix='input')
File "/home/diego/.local/lib/python3.7/site-packages/keras/engine/training_utils.py", line 128, in standardize_input_data
'with shape ' + str(data_shape))
ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (1, 198, 39)

The code used is:
python3 cnn_examples.py

The envinronment is created via "pip3 install -r requirements.txt" as you suggested

I don't know what this problem is, can you help me?
Thanks

[leileileidan]

Hello! I have solved this problem as follows:
In the file of cnn_examlpe.py .Before model.predict_one(),reshapes the shape of the results of get_feature_vector_from_mfcc(filename, flatten=to_flatten) :
mfcc = get_feature_vector_from_mfcc(filename, flatten=to_flatten)
mfcc = mfcc.reshape(mfcc.shape[0], mfcc.shape[1], 1)
print('prediction', model.predict_one(mfcc), 'Actual 3')

[Mahanotrahul]

@leileileidan You should create a pull request for this.

[YoussefHenna]

mfcc = get_feature_vector_from_mfcc(filename, flatten=to_flatten)
mfcc = mfcc.reshape(mfcc.shape[0], mfcc.shape[1], 1)
print('prediction', model.predict_one(mfcc), 'Actual 3')

This fixed another issue for me where I was getting incorrect results for the final prediction. Thanks!