Tensorflow Python Type error let me understand whats wrong
Hello fellow Tensorflow specialists!
I have the following code mainly copied from an example. I've got 38 inputs and 4 outputs in my NN. I want to teach the NN and make predictions. But if I run the program I got an error saying:
b = tf.Variable(tf.float32, [None, n_class]) TypeError: Expected binary or unicode string, got tf.float32
together with:
TypeError: Failed to convert object of type to Tensor. Contents: . Consider casting elements to a supported type.
import csv
import matplotlib as plt
import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
with open('input.csv', "r") as myfile:
reader = csv.reader(myfile, delimiter=",")
data = list(reader)
data = np.asarray(data)
x = data[:,0:39]
y = data[:,39:43]
train_x, test_x, train_y, test_y = train_test_split(x,y, test_size=0.05)
learning_rate = 0.3
training_epochs = 1000
cost_history = np.empty(shape=[1], dtype=float)
n_dim = x.shape[1]
print("n_dim =" , n_dim)
n_class = 2
# model_path =
n_hidden_1 = 60
n_hidden_2 = 60
n_hidden_3 = 60
X = tf.placeholder(tf.float32, [None, n_dim])
W = tf.Variable(tf.zeros([n_dim, n_class]))
b = tf.Variable(tf.float32, [None, n_class])
y_ = tf.placeholder(tf.float32, [None, n_class])
def multilayer_perceptron(x, weights, biases):
layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
layer_1 = tf.nn.sigmoid(layer_1)
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.sigmoid(layer_2)
layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3'])
layer_3 = tf.nn.relu(layer_3)
out_layer = tf.matmul(layer_3, weights['out']) + biases['out']
return out_layer
weights = {
'h1': tf.Variable(tf.truncated_normal([n_dim, n_hidden_1])),
'h2': tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2])),
'h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_3])),
'out': tf.Variable(tf.truncated_normal([n_hidden_3, n_class]))
}
biases = {
'b1' : tf.Variable(tf.truncated_normal([n_hidden_1])),
'b2' : tf.Variable(tf.truncated_normal([n_hidden_2])),
'b3' : tf.Variable(tf.truncated_normal([n_hidden_3])),
'out' : tf.Variable(tf.truncated_normal([n_class]))
}
init = tf.global_variables_initializer()
saver = tf.train.Saver()
cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_))
training_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_func)
sess = tf.Session()
sess.run(init)
mse_history = []
accuracy_history = []
for epoch in range(training_epochs):
sess.run(training_step, feed_dict={x: train_x, y_: train_y})
cost = sess.run(cost_func, feed_dict={x: train_x, y_: train_y})
cost_history = np.append(cost_history, cost)
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
pred_y = sess.run(y, feed_dict={x: test_x})
mse = tf.reduce_mean(tf.square(pred_y - test_y))
mse_ = sess.run(mse)
mse_history.append(mse_)
accuracy = (sess.run(accuracy, feed_dict={x: train_x, y_: train_y}))
accuracy_history.append(accuracy)
print('epoch: ', epoch, ' - cost: ', cost, " - MSE: ", mse_, " - Train Accuracy: ", accuracy)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Test Accuracy: ", (sess.run(accuracy, feed_dict={x: test_x, y_: test_y})))
pred_y = sess.run(y, feed_dict={x: test_x})
mse = tf.reduce_mean(tf.square((pred_y - test_y)))
print ("MSE %.4f" % sess.run(mse))
Do you have any suggestions what to do next?
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