Help: model with various input shapes (csv floats and integer sequences)

TensorFlow General Discussion
,
1

Hello everyone,

I built a model that gets its input from a csv file. Everything works fine when I use the first four features. But as soon as I add the 5th feature, problems arise. The problem with feature 5 is that each entry is a numpy array(shape=(4,288)) stored as a string in the csv file.

The csv file has the following structure:
Feature1,Feature2,Feature3,Feature4,Labels,Feature5
13.37,33.09,-0.08,992.2,nass,“[[1, 160, 246, 255], … ,[1, 160, 246, 255]]”
26.37,33.03,-0.08,992.2,trocken,“[[110, 160, 246, 255], … ,[20, 160, 246, 255]]”

I use pandas to read the csv file.

CODE:

def build_model(num_features: int, num_classes: int) -> Sequential:

    model = Sequential()

    model.add(Dense(units=100, input_shape=(num_features,))) 

    model.add(Activation("relu"))

    model.add(Dense(units=100, input_shape=(num_features,)))

    model.add(Activation("relu"))

    model.add(Dense(units=num_classes))

    model.add(Activation("softmax"))

    model.summary()

    return model


df = pd.read_csv(r'D:\pyenv\Daten\gps_time_Regenverlauf2.csv', skiprows=[1])

df = df.replace(['trocken','feucht','nass','Wasser steht','Schnee(decke)','Schnee(matsch)','duenne Schneeschicht'],[0,1,1,1,2,2,2])

df = df.dropna()

for i in range(0,999):

    df.iat[i,999] = df.iat[i,999].strip("'")

print("Daten:", df.head())

labels = df.pop('Labels')

labels = np.asarray(labels).astype('float32')

data = np.array(df.loc[:1000, ['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5']])

#data = np.array(df.loc[:1000, ['Feature1', 'Feature2', 'Feature3', 'Feature4']])

x_train, x_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.20, random_state=42)

num_features = 5

num_classes = 3

labels_train = to_categorical(labels_train, num_classes=num_classes, dtype=np.float32)

labels_test = to_categorical(labels_test, num_classes=num_classes, dtype=np.float32)

model = build_model(num_features, num_classes)

model.compile(loss= tf.keras.losses.CategoricalCrossentropy() , optimizer=tf.keras.optimizers.Adam(), metrics=["accuracy"])

model.fit(x_train, labels_train, epochs=50, verbose=1,validation_data=(x_test , labels_test))

model.summary()

model.evaluate(x_test_normalized, labels_test, verbose=2)

x_pred = model.predict(x_test_normalized)

I get the following error.

ERROR:

 dense_2 (Dense)             (None, 3)                 303

 activation2 (Activation)   (None, 3)                 0

=================================================================
Total params: 10,903
Trainable params: 10,903Non-trainable params: 0__
Traceback (most recent call last):
  File "classification_DNN.py", line 165, in <module>
    model.fit(x_train, labels_train, epochs=10, verbose=1,validation_data=(x_test , labels_test))
  File "D:\pyenv.venv\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "D:\pyenv.venv\lib\site-packages\tensorflow\python\framework\constant_op.py", line 102, in convert_to_eager_tensor
    return ops.EagerTensor(value, ctx.device_name, dtype)
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float).

How can I make the model work with feature 5 ?

I was thinking about to reshape feature 5 but then I still have a sequence. I read that for sequences I should use a RNN with lstm but then I don’t know how to combine that with the other input features.

All help will be much appreciated.

Cheers, Mo

3

Hi @Moritz

Have you tried to convert the list to a numpy array and change the dtype to float32, this worked for me.
you can do it as :

Feature5=np.asarray(Feature5).astype(np.float32)

4

Hi @Shirshak_Ghatak

thanks for your time. That might be the issue. I’m not sure. I tried different ways to convert the csv string back to an numpy array but it still doesn’t work.

I thought the following line of code would take care of the conversion. Because I only get the Error descriped above if I only use this line:

data = np.array(df.loc[:1000, ['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5']])

But if I try something like this:

I read the csv file with pandas into df and try to remove " ’ " with strip.

for i in range(0,999):
    df.iat[i,10] = df.iat[i,10].strip("'")
    
data = df.pop('Feature5')
data = np.asarray(data).astype('float32')

I get the following ERROR:

Traceback (most recent call last):
  File "classification_DNN_forum.py", line 78, in <module>
    data = np.asarray(data).astype('float32')
ValueError: could not convert string to float: '[[1, 160, 246, 255], [1, 160, 246, 255], ...

But I’m not sure how to do it properly. Any ideas how to fix that?

6

Not sure if this is the best way to solve it but you could try the following method:

ar="[[1,2,3,4],[3,4,6,7]]"
emp=[]
i=0
temp=[]
for str in ar:
    if all([str!='[',str!=']',str!=',']):
        i+=1
        num=int(str)
        temp.append(num)
    if i==4:
        emp.append(temp)
        temp=[]
        i=0
emp=np.array(emp).astype('float32')

Then do the same for all the data points

1 Like
8

Hi @Shirshak_Ghatak

I tried the following

CODE:

for k in range(1,999):
    ar=df.loc[k, ['feature5']].to_string(header=None)
    i=0
    temp=[]
    for str in ar:
        if all([str!='[',str!=']',str!=',']):
            i+=1
            num=int(str)
            temp.append(num)
        if i==4:
            emp.append(temp)
            temp=[]
            i=0
    emp=np.array(emp).astype('float32')

and I get this

ERROR:

classification_DNN_forum.py:91: DeprecationWarning: string or file could not be read to its end due to unmatched data; this will raise a ValueError in the future.
  emp = np.fromstring(ar, dtype='float32', sep=',')
Traceback (most recent call last):
  File "classification_DNN_forum.py", line 98, in <module>
    num=int(str)
ValueError: invalid literal for int() with base 10: 'R'

so I replaced int() with float() and got this

ERROR:

Traceback (most recent call last):
  File "classification_DNN_forum.py", line 98, in <module>
    num=float(str)
ValueError: could not convert string to float: 'R'
9

Could you find where this ‘R’ is coming in your feature5 as I thought it only consisted of numerical 2D matrices type casted as string :sweat_smile:. Then I could help you further

11

@Shirshak_Ghatak thanks for your help! I’m sure your solution works too. I don’t know where the ‘R’ comes from maybe from the header but the header shouldn’t be in ar.

This is how I solved it for now:

import ast
data = np.empty([289,4], int)
for i in range(599):
    my_list = ast.literal_eval(df['feature5'][i])
    step = np.array(my_list)
    data = np.vstack([data, step]) #np.append(data, step, axis=0)
data = np.reshape(data,(-1,289,4))
13

Happy to try and help :grin:

1 Like