I am training a model that adds a couple of layers to the predifined VGGish network (see github repo), so that it can predict the class of input logmel spectrograms extracted from audio files (full code at bottom).
I generate X_train, X_test, y_train, y_test sets from a previous function first and then run the main() codeblock. This predicts the classes of the X_test at line 78 and prints these:
predictions_sigm = logits.eval(feed_dict = {features_input:X_test})
print(predictions)
Prints:
[[ -9.074987 8.840093 -8.426974 ]
[ -9.376444 9.13514 -8.79967 ]
[-10.03653 -7.725624 7.2162223]
[ -9.650997 9.308293 -8.9559 ]
[ 7.789041 -7.8485446 -9.8974285]
[ 7.7869387 -7.850354 -9.899081 ]
[-10.4985485 -8.368322 7.558868 ]
[-10.306433 -8.043555 7.4093537]
[ 7.787068 -7.850254 -9.898217 ]
[ 7.789579 -7.851698 -9.90515 ]
[ 7.787512 -7.8483863 -9.90212 ]
[ -9.28933 9.058059 -8.713937 ]
[ 7.7886 -7.8486743 -9.901876 ]
[ 7.7899137 -7.8464875 -9.899316 ]
[-10.434939 -8.171508 7.459009 ]
[-10.714449 -8.394194 7.642472 ]
[-10.564347 -8.165948 7.6475844]
[ -9.63355 9.158067 -8.794765 ]
[ -9.501944 9.241178 -8.889491 ]]
My main query is how do I get the array to instead print like this, where it returns 0's and 1's or values between -1 to 1 for each prediction, which I can then convert to 0's and 1's:
[[ 0 1 0 ]
[ 0 1 0 ]
[ 0 0 1]
...
[ 0 1 0 ]]
I thought this could be done using predictions_sigm = prediction.eval(...) for this line (78) instead of predictions_sigm = logits.eval(...), as it appeared to be named 'prediction' and use sigmoid some how, at line 27 tf.sigmoid(logits, name='prediction'), but using this gives a 'NameError: name 'prediction' is not defined'.
If presented as a range of values, either -11 to 10 or -1 to 0, are their values useful for something?
Full code:
#run using:
#python vggish_train_demo.py --num_batches 100
_NUM_CLASSES = 3
batch_size = 10
def main(X):
with tf.Graph().as_default(), tf.Session() as sess:
# Define VGGish.
embeddings = vggish_slim.define_vggish_slim(training=FLAGS.train_vggish)
# Define a shallow classification model and associated training ops on top
# of VGGish.
with tf.variable_scope('mymodel'):
# Add a fully connected layer with 100 units. Add an activation function
# to the embeddings since they are pre-activation.
num_units = 100
fc = slim.fully_connected(tf.nn.relu(embeddings), num_units)
# Add a classifier layer at the end, consisting of parallel logistic
# classifiers, one per class. This allows for multi-class tasks.
logits = slim.fully_connected(
fc, _NUM_CLASSES, activation_fn=None, scope='logits')
tf.sigmoid(logits, name='prediction')
# Add training ops.
with tf.variable_scope('train'):
global_step = tf.train.create_global_step()
# Labels are assumed to be fed as a batch multi-hot vectors, with
# a 1 in the position of each positive class label, and 0 elsewhere.
labels_input = tf.placeholder(
tf.float32, shape=(None, _NUM_CLASSES), name='labels')
# Cross-entropy label loss.
xent = tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits, labels=labels_input, name='xent')
loss = tf.reduce_mean(xent, name='loss_op')
tf.summary.scalar('loss', loss)
# We use the same optimizer and hyperparameters as used to train VGGish.
optimizer = tf.train.AdamOptimizer(
learning_rate=vggish_params.LEARNING_RATE,
epsilon=vggish_params.ADAM_EPSILON)
train_op = optimizer.minimize(loss, global_step=global_step)
# Initialize all variables in the model, and then load the pre-trained
# VGGish checkpoint.
sess.run(tf.global_variables_initializer())
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
# The training loop.
features_input = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
for epoch in range(FLAGS.num_batches):
epoch_loss = 0
i=0
while i < len(X_train):
start = i
end = i batch_size
batch_x = np.array(X_train[start:end])
batch_y = np.array(y_train[start:end])
_, c = sess.run([train_op, loss], feed_dict={features_input: batch_x, labels_input: batch_y})
epoch_loss = c
i =batch_size
print('Epoch', epoch 1, 'completed out of',FLAGS.num_batches,', loss:',epoch_loss)
correct = tf.equal(tf.argmax(logits, 1), tf.argmax(labels_input, 1))
print('Accuracy:',accuracy.eval({features_input:X_test, labels_input:y_test}))
predictions = logits.eval(feed_dict = {features_input:X_test})
print(predictions) #shows table of predictions
#Saves csv file of table of predictions for test data
time = datetime.now().strftime('%H.%M.%S')
np.savetxt("test_predictions_" time ".csv", predictionsm, delimiter=",") #put 'r"r'C:\Users\bw339\...\test_predictions' to save in a different folder
if __name__ == '__main__':
tf.app.run()
#think the 'An exception has occurred, use %tb to see the full traceback.' is a jupyter thing, hopefully won't happen
#when run in conda or bash
Edit for ahmet hamza emra
def main(X):
with tf.Graph().as_default(), tf.Session() as sess:
# Define VGGish.
embeddings = vggish_slim.define_vggish_slim(training=FLAGS.train_vggish)
#embeddings = vggish_slim.define_vggish_slim(features_tensor= X_train, training=FLAGS.train_vggish) #gives an error that arrays are not right type. no idea why as the shpae of X[0] matches what vggish_slim_define() asks for
#prediction = vggish_slim.define_vggish_slim(X)
# Define a shallow classification model and associated training ops on top
# of VGGish.
with tf.variable_scope('mymodel'):
# Add a fully connected layer with 100 units. Add an activation function
# to the embeddings since they are pre-activation.
num_units = 100
fc = slim.fully_connected(tf.nn.relu(embeddings), num_units)
# Add a classifier layer at the end, consisting of parallel logistic
# classifiers, one per class. This allows for multi-class tasks.
#logits = slim.fully_connected( ### logits threw me, would be easier to name this 'end model' or something
# fc, _NUM_CLASSES, activation_fn=None, scope='logits')
#tf.sigmoid(logits, name='prediction')
linear_out= slim.fully_connected(
fc, _NUM_CLASSES, activation_fn=None, scope='linear_out')
logits = tf.sigmoid(logits, name='logits')
# Add training ops.
with tf.variable_scope('train'):
global_step = tf.train.create_global_step()
# Labels are assumed to be fed as a batch multi-hot vectors, with
# a 1 in the position of each positive class label, and 0 elsewhere.
labels_input = tf.placeholder(
tf.float32, shape=(None, _NUM_CLASSES), name='labels')
# Cross-entropy label loss.
xent = tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits, labels=labels_input, name='xent') ###=labels is selecting my 'y', logits is like a precursor to predictions?
loss = tf.reduce_mean(xent, name='loss_op')
tf.summary.scalar('loss', loss)
# We use the same optimizer and hyperparameters as used to train VGGish.
optimizer = tf.train.AdamOptimizer(
learning_rate=vggish_params.LEARNING_RATE,
epsilon=vggish_params.ADAM_EPSILON)
train_op = optimizer.minimize(loss, global_step=global_step)
# Initialize all variables in the model, and then load the pre-trained
# VGGish checkpoint.
sess.run(tf.global_variables_initializer()) ### this starts the session appaz
vggish_slim.load_vggish_slim_checkpoint(sess, FLAGS.checkpoint)
# The training loop.
features_input = sess.graph.get_tensor_by_name(
vggish_params.INPUT_TENSOR_NAME)
for epoch in range(FLAGS.num_batches):
epoch_loss = 0
i=0
while i < len(X_train):
start = i
end = i batch_size
batch_x = np.array(X_train[start:end])
batch_y = np.array(y_train[start:end])
_, c = sess.run([train_op, loss], feed_dict={features_input: batch_x, labels_input: batch_y})
epoch_loss = c
i =batch_size
print('Epoch', epoch 1, 'completed out of',FLAGS.num_batches,', loss:',epoch_loss)
#Get accuracy if executed on test data
correct = tf.equal(tf.argmax(logits, 1), tf.argmax(labels_input, 1)) #This line returns the max value of each array, which we want o be the same (think the prediction/logits is value given to each class with the highest value being the best match)
accuracy = tf.reduce_mean(tf.cast(correct, 'float')) #changes correct to type: float
print('Accuracy:',accuracy.eval({features_input:X_test, labels_input:y_test})) #TF is smart so just knows to feed it through the model without us seeming to tell it to. .eval() uses the current session which I guess is my model?
#Save predictions for test data
predictions_sigm = logits.eval(feed_dict = {features_input:X_test}) #not really _sigm, change back later
#print(predictions_sigm) #shows table of predictions
test_preds = pd.DataFrame(predictions_sigm, columns = col_names) #converts predictions to df
true_class = np.argmax(y_test, axis = 1) #This saves the true class
test_preds['True class'] = true_class #This adds true class to the df
print(test_preds)
#Saves csv file of table of predictions for test data. NB. header will not save when using np.text for some reason
time = datetime.now().strftime('%H.%M.%S')
#np.savetxt("test_predictions_" time ".csv", test_preds.values, delimiter=",") #put 'r"r'C:\Users\bw339\...\test_predictions' to save in a different folder
##Save model
#saver = tf.train.Saver()
#saver.save(sess, 'my-test-model')
if __name__ == '__main__':
tf.app.run()
#think the 'An exception has occurred, use %tb to see the full traceback.' is a jupyter thing, hopefully won't happen
#when run in conda or bash
CodePudding user response：
You are outputing the linear-layer before the sigmoid. Change the code as following:
# Add a classifier layer at the end, consisting of parallel logistic
# classifiers, one per class. This allows for multi-class tasks.
linear_out= slim.fully_connected(
fc, _NUM_CLASSES, activation_fn=None, scope='linear_out')
logits = tf.sigmoid(linear_out, name='logits')
This will ensure you output the values between 0 and 1.
Note: Your evaluation is not considering multi-class classification, argmax will return the index of the largest value which in your case will be single output.