I am new to Keras and trying to write my first code. I want to understand what 'model.predict' should return. Consider a simple model below.
model = keras.Sequential()
model.add(keras.layers.Dense(12, input_dim=232, activation='relu'))
model.add(keras.layers.Dense(232, activation='relu'))
model.add(keras.layers.Dense(1, activation='sigmoid'))
# compile the keras model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit the keras model on the dataset
model.fit(vSignal, vLabels, epochs=15, batch_size=100 )
# evaluate the keras model
_, accuracy = model.evaluate(vSignal, vLabels)
print('Accuracy: %.2f' % (accuracy*100))
pred=model.predict(vSignalT)
Consider we train the "model" with "vSignal" and "vLabels" as shown above. Now consider that the accuracy of the model as given by model.evaluate is 100%. Now if we give same data 'vSignal' to 'model.predict' should we get the 'vLabels' return?
pred=model.predict(vSignalT) returns a numpy arrays of predictions.
each row consists of one of the vlabels that the model predicted.
for more information refer to here
save return value of fit function:
hist = model.fit(vSignal, vLabels, epochs=15, batch_size=100 );
then check the
hist.history["accuracy"]
Related
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 have trained a keras LSTM model. But after training, all i get is the final parameters of the models after training with 10 epochs and batch size=120. How can i get intermediate parameter after a batch keras.
Example: after 120 sample in each batch i can get the intermediate parameter of this step.
I have tried callback method and backend in keras, but i do not know how to get the
'''python
model = Sequential()
model.add(Embedding(max_features, 32))
#model.add(LSTM(32, return_sequences=True, input_shape=(1,texts.shape[0])))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])
history_ltsm = model.fit(texts_train, y_train, epochs=10, batch_size=120, validation_split=0.2)
'''
I expected the model run step by step based on each batch to show the intermediate parameters, but not the all epochs.
Thank you very much!
I studied Keras and created my first neural network model as the following:
from keras.layers import Dense
import keras
from keras import Sequential
from sklearn.metrics import accuracy_score
tr_X, tr_y = getTrainingData()
# NN Architecture
model = Sequential()
model.add(Dense(16, input_dim=tr_X.shape[1]))
model.add(keras.layers.advanced_activations.PReLU())
model.add(Dense(16))
model.add(keras.layers.advanced_activations.PReLU())
model.add(Dense(1, activation='sigmoid'))
# Compile the Model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the Model
model.fit(tr_X, tr_y, epochs=1000, batch_size=200, validation_split=0.2)
# ----- Evaluate the Model (Using UNSEEN data) ------
ts_X, ts_y = getTestingData()
yhat_classes = model.predict_classes(ts_X, verbose=0)[:, 0]
accuracy = accuracy_score(ts_y, yhat_classes)
print(accuracy)
I am not sure about the last portion of my code, i.e., model evaluation using model.predict_classes() where new data are loaded via a custom method getTestingData(). See my goal is to test the final model using new UNSEEN data to evaluate its prediction. My question is about this part: Am I evaluating the model correctly?
Thank you,
Yes, that is correct. You can use predict or predict_classes to get the predictions on test data. If you need the loss & metrics directly, you can use the evaluate method by feeding ts_X and ts_y.
y_pred = model.predict(ts_X)
loss, accuracy = model.evaluate(ts_X, ts_y)
https://keras.io/models/model/#predict
https://keras.io/models/model/#evaluate
Difference between predict & predict_classes: What is the difference between "predict" and "predict_class" functions in keras?
I have a text dataset, that contains 6 classes. for each sample, I have the percent value and sum of the 6 percent values is 100% (features are related to each other). For example :
{A:16, B:35, C:7, D:0, E:3, F:40}
how can I feed a deep learning algorithm with this dataset?
I actually want the prediction to be exactly in the shape of training data.
Here is what you can do:
First of all, normalize all of your labels and scale them between 0-1.
Use a softmax layer for prediction.
Here is some code in Keras for intuition:
model = Sequential()
model.add(Dense(100, input_dim = x.shape[1], activation='relu'))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
I modified the existing activation function and using it in the Convolutional layer of the Neural Network. I would like to know how does it perform compared to the existing activation function.Is there any method/function to plot in a graph the results(matrix values) after each Neural network layer,so that I could customise my activation function according to the values for better results?
model = Sequential()
e = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=max_length, trainable=False)
model.add(e)
model.add(Conv1D(64,kernel_size,padding='valid',activation=newactivation,strides=1))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(Conv1D(256,kernel_size,padding='valid',activation=newactivation,strides=1))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(Bidirectional(GRU(gru_output_size, dropout=0.2, recurrent_dropout=0.2)))
model.add(Bidirectional(LSTM(lstm_output_size)))
model.add(Dense(nclass, activation='softmax'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
print(model.summary())
model.fit(padded_docs,y_train, epochs=epoch_size, verbose=0)
loss, accuracy = model.evaluate(tpadded_docs, y_test, verbose=0)
I cannot comment yet so I post this as an answer:
Refer to the Keras FAQ: "How can I obtain the output of an intermediate layer?"
It shows you how you can access the output of each layer. If you are using the version that uses the keras function, you can even access the output of the model in the learning phase (if your model contains layer that behave differently in training vs. testing).