I am able to perform classification with this code. It outputs the probability for each output labels. But I need to convert this so that it can predict the values. That is, I want to add a regression layer at the end instead of softmax. How can I achieve this? Let's say for example I trained the model for label 1,2,3,4,5. But I want the model to predict the values beyond those 5 labels. Example, Given the input, the model may predict 1.3 or 2.5, etc. I want a continuous output rather than a discrete output.
Update
I am trying to achieve a suggested solution from this question Here
Let's say I have a training data. I train the model for whole number temperatures like 1,2,3,4,5 degrees. Basically, Those output temperatures are the labels. How can I predict the values that lies between two temperatures like 2.5 degree. It is not possible to train for every values of temperature. How can I achieve this?
My model gives probability of each class predicted
Temp Probability
1 .01
2 .05
3 .56
4 .24
5 .14
I want my model to predict the temperature values like 1.2, 2.7, etc. instead of predicting the probability of each class.
input_height = 1 # 1-Dimensional convulotion
input_width = 90 #window
num_labels = 5 #output labels
num_channels = 8 #input columns
batch_size = 10
kernel_size = 60
depth = 60
num_hidden = 1000
learning_rate = 0.0001
training_epochs = 8
total_batches = train_x.shape[0] # batch_size
X = tf.placeholder(tf.float32, shape=[None,input_height,input_width,num_channels],name="input")
# X = tf.placeholder(tf.float32, shape=[None,input_width * num_channels], name="input")
# X_reshaped = tf.reshape(X,[-1,1,90,3])
Y = tf.placeholder(tf.float32, shape=[None,num_labels])
c = apply_depthwise_conv(X,kernel_size,num_channels,depth)
p = apply_max_pool(c,20,2)
c = apply_depthwise_conv(p,6,depth*num_channels,depth//10)
shape = c.get_shape().as_list()
c_flat = tf.reshape(c, [-1, shape[1] * shape[2] * shape[3]])
f_weights_l1 = weight_variable([shape[1] * shape[2] * depth * num_channels * (depth//10), num_hidden])
f_biases_l1 = bias_variable([num_hidden])
f = tf.nn.tanh(tf.add(tf.matmul(c_flat, f_weights_l1),f_biases_l1))
out_weights = weight_variable([num_hidden, num_labels])
out_biases = bias_variable([num_labels])
y_ = tf.nn.softmax(tf.matmul(f, out_weights) + out_biases,name="y_")
loss = -tf.reduce_sum(Y * tf.log(y_))
optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(loss)
correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1)) #difference between correct output and expected output
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
cost_history = np.empty(shape=[1], dtype=float)
with tf.Session() as session:
tf.global_variables_initializer().run()
for epoch in range(training_epochs):
for b in range(total_batches):
offset = (b * batch_size) % (train_y.shape[0] - batch_size)
batch_x = train_x[offset:(offset + batch_size), :, :, :]
batch_y = train_y[offset:(offset + batch_size), :]
_, c = session.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y})
cost_history = np.append(cost_history, c)
print "Epoch: ", epoch, " Training Loss: ", c, " Training Accuracy: ",session.run(accuracy, feed_dict={X: train_x, Y: train_y})
print "Testing Accuracy:", session.run(accuracy, feed_dict={X: test_x, Y: test_y})
