keras

问题 I am trying to use keras to fit a CNN model to classify 2 classes of data . I have imbalanced dataset I want to balance the data. I don't know can I use class_weight in model.fit_generator . I wonder if I used class_weight="balanced" in model.fit_generator The main code : def generate_arrays_for_training(indexPat, paths, start=0, end=100): while True: from_=int(len(paths)/100*start) to_=int(len(paths)/100*end) for i in range(from_, int(to_)): f=paths[i] x = np.load(PathSpectogramFolder+f) x =

问题 I'm learning tensorflow 2.0 from its older versions. I found tensorflow model is changed Sequential-base from Class-base. But I want to use Class-base model because it is easy to read for me. I want to try translate : https://www.tensorflow.org/beta/tutorials/keras/basic_text_classification_with_tfhub embedding = 'https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1' hub_layer = hub.KerasLayer(embedding, input_shape=[], dtype=tf.string, trainable=True) # hub_layer(train_example_batch[:3]

问题 I know there are a bunch of questions on this problem and I have read some of those but none of them worked for me. I am trying to build a model with the following architecture: The code is as follows: token_inputs = Input((32,), dtype=tf.int32, name='input_ids') mask_inputs = Input((32,), dtype=tf.int32, name='attention_mask') seg_inputs = Input((32,), dtype=tf.int32, name='token_type_ids') seq_out, _ = bert_model([token_inputs, mask_inputs, seg_inputs]) bd = Bidirectional(LSTM(units=50,

问题 I have the code shown below, but get the following error: ValueError: could not convert string to float: BRAF Provided that this is a sample of my data ( | is just a separator I added here for demonstration, you can imagine each value in a separate cell in a CSV file): c.401C>T | skin | 23:141905805-141905805 | 9947 | BRAF Could the strings be the issue? How can I read and pass strings in this case? from keras.models import Sequential from keras.layers import Dense from keras.models import

问题 I'm learning tensorflow 2.0 from its older versions. I found tensorflow model is changed Sequential-base from Class-base. But I want to use Class-base model because it is easy to read for me. I want to try translate : https://www.tensorflow.org/beta/tutorials/keras/basic_text_classification_with_tfhub embedding = 'https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1' hub_layer = hub.KerasLayer(embedding, input_shape=[], dtype=tf.string, trainable=True) # hub_layer(train_example_batch[:3]

问题 I know there are a bunch of questions on this problem and I have read some of those but none of them worked for me. I am trying to build a model with the following architecture: The code is as follows: token_inputs = Input((32,), dtype=tf.int32, name='input_ids') mask_inputs = Input((32,), dtype=tf.int32, name='attention_mask') seg_inputs = Input((32,), dtype=tf.int32, name='token_type_ids') seq_out, _ = bert_model([token_inputs, mask_inputs, seg_inputs]) bd = Bidirectional(LSTM(units=50,

问题 I have the code shown below, but get the following error: ValueError: could not convert string to float: BRAF Provided that this is a sample of my data ( | is just a separator I added here for demonstration, you can imagine each value in a separate cell in a CSV file): c.401C>T | skin | 23:141905805-141905805 | 9947 | BRAF Could the strings be the issue? How can I read and pass strings in this case? from keras.models import Sequential from keras.layers import Dense from keras.models import

问题 I'm using Keras in Python 3. The issue I'm having seems to be similar to many others, and the best I can tell I might need to use Flatten(), though I am not seeing how to set the parameters correctly. I get the error: ValueError: Error when checking target: expected dense_2 to have shape (4,) but got array with shape (1,) My data is not of images (yet) but they are sequences I've turned in to data frames. model = Sequential() model.add(Dense(30, input_dim=16, activation='relu')) model.add

问题 I am facing below error when trying to train a multi-class classification model ( 4 classes) for Image dataset. Even though my output tensor is of shape 4 I am facing below issue. Please let me know how to fix this issue. Epoch 1/10 --------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) <ipython-input-30-01c6f78f4d4f> in <module> 4 epochs=epochs, 5 validation_data=val_data_gen, ----> 6 validation_steps=total_val //

问题 I have created model using Keras. Here is the associated code. - https://github.com/CVxTz/ECG_Heartbeat_Classification/blob/master/code/baseline_mitbih.py I could run it and get model accuracy. IT works as expect for train and test data. Now I Want to test with out sample record and get prediction result. How do I do this? My code - df_train = pd.read_csv("mitbih_train.csv", header=None) df_train = df_train.sample(frac=1) df_test = pd.read_csv("mitbih_test.csv", header=None) Y = np.array(df