I'm trying to plot a confusion matrix to analyse my train and test and I'm having difficulties to print/plot the matrix. I'm using convolutional neural networks with Tensorflow for classification, and I have 3 labels to classify.
That's how I'm trying to print it:
true_class = tf.argmax(y, 1)
predicted_class = tf.argmax(prediction, 1)
confusion = tf.confusion_matrix(true_class, predicted_class, 3)
print(confusion)
But the print returns me the following result:
Tensor("confusion_matrix/SparseTensorDenseAdd:0", shape=(3, 3), dtype=int32)
Then I searched for people with the same problem and I tried doing this:
true_class = tf.argmax(y, 1)
predicted_class = tf.argmax(prediction, 1)
confusion = tf.confusion_matrix(true_class, predicted_class, 3)
print('Confusion Matrix: \n\n', tf.Tensor.eval(confusion,feed_dict=None, session=sess))
And it gives me the following error:
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float
[[{{node Placeholder}}]]
My code:
def convolutional_neural_network(x):
number = calc()
weights = {'W_conv1': tf.Variable(tf.random_normal([3, 3, 3, 1, 32])),
'W_conv2': tf.Variable(tf.random_normal([3, 3, 3, 32, 64])),
'W_fc': tf.Variable(tf.random_normal([number, 1024])),
'out': tf.Variable(tf.random_normal([1024, n_classes]))}
biases = {'b_conv1': tf.Variable(tf.random_normal([32])),
'b_conv2': tf.Variable(tf.random_normal([64])),
'b_fc': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([n_classes]))}
x = tf.reshape(x, shape=[-1, IMG_SIZE_PX, IMG_SIZE_PX, SLICE_COUNT, 1])
conv1 = tf.nn.relu(conv3d(x, weights['W_conv1']) + biases['b_conv1'])
conv1 = maxpool3d(conv1)
conv2 = tf.nn.relu(conv3d(conv1, weights['W_conv2']) + biases['b_conv2'])
conv2 = maxpool3d(conv2)
fc = tf.reshape(conv2, [-1, number])
fc = tf.nn.relu(tf.matmul(fc, weights['W_fc']) + biases['b_fc'])
fc = tf.nn.dropout(fc, keep_rate)
output = tf.matmul(fc, weights['out']) + biases['out']
return output
def train_neural_network(x):
much_data = np.load('muchdata-50-50-30-pre.npy', allow_pickle=True)
train_data = much_data[400:410]
validation_data = much_data[390:399]
prediction = convolutional_neural_network(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(
logits=prediction, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
hm_epochs = 1
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(hm_epochs):
epoch_loss = 0
for data in train_data:
X = data[0]
Y = data[1]
_, c = sess.run([optimizer, cost], feed_dict={x: X, y: Y})
epoch_loss += c
print('Epoch', epoch + 1, '/', hm_epochs, '. Loss:', epoch_loss)
true_class = tf.argmax(y, 1)
predicted_class = tf.argmax(prediction, 1)
confusion = tf.confusion_matrix(true_class, predicted_class, 3)
print('Confusion Matrix: \n\n', tf.Tensor.eval(confusion,feed_dict=None, session=sess))
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
saver = tf.train.Saver()
saver.save(sess, '../api/modelo')
print('Accuracy:', accuracy.eval(
{x: [i[0] for i in validation_data], y: [i[1] for i in validation_data]}))
If anyone can help me figure out what's happening, I'll be very grateful! I'm new to this topic and I'm really struggling.
Thank you so much!
You need to feed values to the placeholders in feed_dict. Can you try replacing the following line of your code,
print('Confusion Matrix: \n\n', tf.Tensor.eval(confusion,feed_dict=None, session=sess))
with the following, where your_X and your_Y are your test input and labels which we need to plot the confusion matrix on.
print(sess.run(confusion, feed_dict={x:your_X, y:your_Y}))