python - Keras Model Dense Input Shape Throwing Error -

i have feature vector shape x_train.shape (52, 54)

when train keras model throws me error as:

valueerror: error when checking model input: expected dense_109_input have shape (none, 52) got array shape (52, 54) 

i have tried can think of scanned stack overflow problem still persists. code as:

import pandas pd keras.models import sequential keras.layers import dense sklearn.model_selection import train_test_split sklearn.metrics import accuracy_score  ##### reading csv #####   data = pd.read_csv('dataset/emotion_data.csv')  x = data.ix[:, 4:] y = data['label']  ##### normalizing ##### featurename = list(x) name in featurename:     x[name] = (x[name] - min(x[name]))/(max(x[name]) - min(x[name]))  x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=3)  ##### model ##### model = sequential()  model.add(dense(100, input_shape=(54,), activation='relu')) model.add(dense(100, activation='relu')) model.add(dense(1, activation='softmax'))  model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])  model.fit(x_train, y_train) prediction = model.predict(x_test) print(accuracy_score(y_test, prediction)) 

if interested in data head

in[42]: x_train.head() out[42]:         tempo  total_beats  average_beats  chroma_stft_mean  chroma_stft_std  \ 35  0.438961     0.480897       0.505383          0.504320         0.938452    34  0.520000     0.552580       0.500670          0.581778         0.680247    63  0.477551     0.361328       0.334990          0.705472         0.357676    27  0.477551     0.345419       0.309433          0.492245         0.728405    43  0.520000     0.530305       0.495715          0.306097         0.663995         chroma_stft_var  chroma_cq_mean  chroma_cq_std  chroma_cq_var  \ 35         0.932494        0.975206       0.394472       0.366960    34         0.657810        0.654770       0.550766       0.522269    63         0.333977        0.495473       0.618748       0.591578    27         0.707998        0.644147       0.628125       0.601222    43         0.640980        0.591299       0.639918       0.613379         chroma_cens_mean    ...       zcr_var  harm_mean  harm_std  harm_var  \ 35          0.964034    ...      0.381363   0.021468  0.426776  0.225840    34          0.755071    ...      0.213207   0.021598  0.115191  0.031476    63          0.704930    ...      0.197960   0.021620  0.350194  0.163286    27          0.715832    ...      0.247092   0.022253  0.319208  0.140714    43          0.784991    ...      0.221276   0.021777  0.656981  0.471881         perc_mean  perc_std  perc_var  frame_mean  frame_std  frame_var   35   0.362241  0.673257  0.467421    0.343459   0.174215   0.048846   34   0.365434  0.152561  0.031588    0.091940   0.088991   0.018342   63   0.340043  0.320664  0.116833    0.097610   0.077334   0.015154   27   0.372315  0.604247  0.380492    0.995443   1.000000   1.000000   43   0.377154  0.529161  0.296033    0.122519   0.089255   0.018417    [5 rows x 54 columns] 

it seems simple problem. try first layer of network:

keras.add(dense(100, input_shape=(54,), activation='relu')) 

your problem came fact input_shape should input of single example. have 52 examples of length 54. why there should input_shape=(54,).