My Keras sequential model produces nearly the same predictions for all inputs, when I use predict_proba.
It seems like the model learns nothing, bescause the loss stays nearly the same during training and the predicted probabilites roughly match the class Distribution of my Labels.
Output:
[[ 0.12883131 0.44906539 0.42210329]
[ 0.12883131 0.44906539 0.42210332]
[ 0.12883131 0.44906539 0.42210329]
...,
[ 0.12883131 0.44906539 0.42210329]
[ 0.12883131 0.44906539 0.42210332]
[ 0.12883131 0.44906539 0.42210332]]
I try to solve a document classification task and my Input is a sequence of integers, which represents word indizes received from short texts.
It's a Multi class Problem with 3 classes (as you can see)
anything wrong in general with my model params?
model:
embedding_size = 200
model_cat_cross = Sequential()
# Input Layer
model_cat_cross.add(Embedding(input_dim=vocab_size, output_dim=32))
# LSTM Layer
model_cat_cross.add(LSTM(units=embedding_size,
dropout= 0.2,
recurrent_dropout= 0.2))
# Output Layer
model_cat_cross.add(Dense(units=labels.shape[1],
activation='softmax'))
# Modellparameter
model_cat_cross.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['categorical_accuracy'])
print(model_cat_cross.summary())
# Training des Modells und Cross Validierung
model_cat_cross.fit(X_train, y_train, validation_data=(X_test, y_test),
class_weight={0:3,1:1,2:1},
validation_split=0.1,
epochs=2,
batch_size=32,
shuffle=1)
# save model to single file
#model.save('lstm_model_v1.h5')
Related
I am building a model for my multiclass text classification problem which looks like this:
model = tf.keras.Sequential()
model.add(hub_layer)
for units in [128, 128, 64 , 32]: # automatically adding layers through a for loop
model.add(tf.keras.layers.Dense(units, activation='relu'))
model.add(tf.keras.layers.Dropout(0.3))
model.add(tf.keras.layers.Dense(18, activation='softmax'))
model.summary()
# Optimiser Function
model.compile(optimizer='adam',
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
history = model.fit(train_data_f,
epochs=20,
validation_data=test_data_f,
verbose=1,
class_weight=class_weights
)
Once the build is done, I would like to do the following:
Save the model
Load the model and apply it to an unseen text (For Example: the column on which I would want to apply this model is df['Unseen_Text]
how can I achieve this?
Why is my code outputting nan? I'm using a sequential model with a 30x1 input vector and a single value output. I'm using tensorflow and python. This is one of my firs
While True:
# Define a simple sequential model
def create_model():
model = tf.keras.Sequential([
keras.layers.Dense(30, activation='relu',input_shape=(30,)),
keras.layers.Dense(12, activation='relu'),
keras.layers.Dropout(0.2),
keras.layers.Dense(7, activation='relu'),
keras.layers.Dense(1, activation = 'sigmoid')
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
return model
# Create a basic model instance
model = create_model()
# Display the model's architecture
model.summary()
train_labels=[1]
test_labels=[1]
train_images= [[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]]
test_images=[[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]]
model.fit(train_images,
train_labels,
epochs=10,
validation_data=(test_images, test_labels),
verbose=1)
print('predicted:',model.predict(train_images))
You are using SparseCategoricalCrossentropy. It expects labels to be integers starting from 0. So, you have only one label 1, but it means you have at least two categories - 0 and 1. So you need at least two neurons in the last layer:
keras.layers.Dense(2, activation = 'sigmoid')
( If your goal is classification, you should maybe consider to use softmax instead of sigmoid, without from_logits=True )
You're using the wrong loss function for those labels. You need to use BinaryCrossentropy.
Change:
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
To:
model.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy,
metrics=[tf.keras.metrics.BinaryAccuracy()])
I need outputs at every recurrent layer and my setup is as follows:
100 training examples, 3 time steps per example, and 20-d feature vector for each individual element.
x_train: (100,3,20)
y_train: (100,20)
LSTM architecture:
model.add(LSTM(20, input_shape=(3,20), return_sequences=True))
model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['accuracy'])
model.summary()
Training:
history = model.fit(x_train, y_train, epochs=50, validation_data=(x_test, y_test))
Error:
ValueError: Dimensions must be equal, but are 20 and 3 for '{{node Equal}} = Equal[T=DT_FLOAT, incompatible_shape_error=true](IteratorGetNext:1, Cast_1)' with input shapes: [?,20], [?,3].
Please help me with the correct input/output LSTM dimensions.
Thanks
LSTM(20, input_shape=(3,20), return_sequences=True) takes as input shape (100,3,20) and returns (100,3,20). Your target output is however encoded as (100,20).
From the dimensions, I assume you want to map each sequence to a non-sequence, i.e. you can do:
LSTM(20, input_shape=(3,20), return_sequences=False)
This will return the final hidden state, i.e. a shape of (100,20) which matches your target output.
"ValueError: Error when checking target: expected activation_81 to have shape (1,) but got array with shape (7,)"
I am performing a multiclass classification of 7 classes for speech emotion classification using a neural network, but it fails at this point
cnnhistory=model.fit(x_traincnn,
y_train,
batch_size=16,
epochs=700,
validation_data=(x_testcnn, y_test),
callbacks=[mcp_save, lr_reduce])
at the line callbacks=[mcp_save, lr_reduce]
mcp_save being
mcp_save = ModelCheckpoint('model/aug_noiseNshift_2class2_np.h5',
save_best_only=True, monitor='val_loss', mode='min')
and lr_reduce being
lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=20, min_lr=0.000001)
Final layer of NN
Dense(7) for 7 classes
model.add(Dense(7))
model.add(Activation('softmax'))
opt = keras.optimizers.SGD(lr=0.0001, momentum=0.0, decay=0.0, nesterov=False)
compiled model using
model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=['accuracy', fscore])
I have already transformed the dataset, with normalised values, changed the loss function to 'sparse_categorical_crossentropy' from 'categorical_crossentropy'. Nothing has worked just pushed the error from activation_9 to activation_18 to activation_45 to activation_54 to now activation_81. But the error is still there.
Any help would be highly appreciated!
I am new to neural networks.
TIA
If you have labels as numbers, that means y_train has shape (samples, 1) and you should use 'sparse_categorical_crossentropy'.
If you have labels as one-hot encodings, that means y_train has shape (samples, 7) and you should use 'categorical_crossentropy'.
I am trying to fine-tune pre-trained Inceptionv3 in Keras for a multi-label (17) prediction problem.
Here's the code:
# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)
# add a new top layer
x = base_model.output
predictions = Dense(17, activation='sigmoid')(x)
# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)
# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(loss='binary_crossentropy', # We NEED binary here, since categorical_crossentropy l1 norms the output before calculating loss.
optimizer=SGD(lr=0.0001, momentum=0.9))
# Fit the model (Add history so that the history may be saved)
history = model.fit(x_train, y_train,
batch_size=128,
epochs=1,
verbose=1,
callbacks=callbacks_list,
validation_data=(x_valid, y_valid))
But I got into the following error message and had trouble deciphering what it is saying:
ValueError: Error when checking target: expected dense_1 to have 4
dimensions, but got array with shape (1024, 17)
It seems to have something to do with that it doesn't like my one-hot encoding for the labels as target. But how do I get 4 dimensions target?
It turns out that the code copied from https://keras.io/applications/ would not run out-of-the-box.
The following post has helped me:
Keras VGG16 fine tuning
The changes I need to make are the following:
Add in the input shape to the model definition base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(299,299,3)), and
Add a Flatten() layer to flatten the tensor output:
x = base_model.output
x = Flatten()(x)
predictions = Dense(17, activation='sigmoid')(x)
Then the model works for me!