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Simple -- but not simplistic. Keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter.
Flexible -- Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon what you've already learned.
Powerful -- Keras provides industry-strength performance and scalability: it is used by organizations and companies including NASA, YouTube, or Waymo.
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QUESTION
When recognizing hand gesture classes, I always get the same class in Keras
Asked 2022-Feb-22 at 13:49When recognizing hand gesture classes, I always get the same class, although I tried changing the parameters and even passed the data without normalization:
df_train = pd.read_csv('train_dataset.csv')
df_train = df_train.drop(columns=['Unnamed: 0'], axis=1)
df_train = df_train.fillna(0)
x_train = df_train.drop(['y'], axis=1)
y_train = df_train['y']
x_train = x_train / 310
model = keras.models.Sequential([Dense(32, input_shape=(42,), activation='relu'),
Dense(64, activation='relu'),
Dense(6, activation='softmax')])
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train_cat, batch_size=16, epochs=9, validation_split=0.2)
model.save("gestures_model.h5")
Here is a main code:
REV_CLASS_MAP = {
0: "up",
1: "down",
2: "right",
3: "left",
4: "forward",
5: "back"
}
def mapper(val):
return REV_CLASS_MAP[val]
if len(data[data.index(new_row)]) > 0:
df = pd.DataFrame(data, columns=columns)
df = df.fillna(0)
df = df / 310
pred = model.predict(df)
move_code = np.argmax(pred[0])
user_move_name = mapper(move_code)
print(user_move_name)
Here is an example of input data:
56,172,72,169,88,155,100,144,111,139,78,120,81,94,82,77,82,62,66,120,62,104,62,124,64,136,54,122,50,110,52,130,55,139,43,126,40,114,42,129,45,137,0
What am I doing wrong and how to fix it? I noticed that in my data there are rows in which there is only one number. Could this be the cause of my problem? ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ P.S I am new to neural networks and keras.
New
Here is df_train before processing:
,x11,x21,x12,x22,x13,x23,x14,x24,x15,x25,x16,x26,x17,x27,x18,x28,x19,x29,x110,x210,x111,x211,x112,x212,x113,x213,114,214,115,x215,x116,x216,x117,x217,x118,x218,x119,x219,x120,x220,x121,x221,y
56,172,72,169,88,155,100,144,111,139,78,120,81,94,82,77,82,62,66,120,62,104,62,124,64,136,54,122,50,110,52,130,55,139,43,126,40,114,42,129,45,137,0
...
84,166,96,158,108,143,108,131,101,127,87,145,87,128,90,118,94,111,74,147,76,119,81,114,84,115,64,148,66,120,72,119,74,124,56,148,57,124,61,124,63,129,5
Here is df_train after processing:
x11 x21 x12 x22 x13 x23 x14 ... x119 x219 x120 x220 x121 x221 y
0 56 175 73 168 88 155 101 ... 42 113 44 130 47 138 0.0
1 172 72 169 88 155 100 144 ... 114 42 129 45 137 0 0.0
2 172 72 169 88 155 100 144 ... 114 42 129 45 137 0 0.0
3 174 74 167 89 155 101 144 ... 115 44 130 46 137 0 0.0
4 174 74 169 90 155 101 144 ... 114 44 128 46 136 0 0.0
.. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
843 166 96 158 108 143 108 131 ... 124 61 124 63 129 5 0.0
844 166 94 158 105 145 104 132 ... 128 58 130 59 134 5 0.0
845 164 90 155 101 141 100 129 ... 126 55 129 57 134 5 0.0
846 158 88 152 99 140 96 128 ... 142 54 150 58 146 5 0.0
847 158 88 152 99 140 96 128 ... 142 54 150 58 146 5 0.0
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
ANSWER
Answered 2022-Feb-17 at 18:48All rows need the same data size, of course some values can be empty in csv.
feature1, feature2, feature3,y
aaa,bbb,3.0,2.0
bbb, ,4.1, 3.1
You need to impute empty values by using for example most frequent value for categorical values or median for numerical values. Predicted value cant be empty
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