Jul-11-2017, 02:09 PM
(This post was last modified: Jul-11-2017, 02:19 PM by ichabod801.)
my question here
I try to use CSV file as the input of the neural network. But I got the warming as 'could not convert string to float: 'train2.CSV'
' My CSV files contain 15 columns. I have no idea how to convert it to float type. My data is over 10K.
Here is my CSV data looks like
12.71 17.76 91.42 30.78 0 0 6.9 0 978 3576 205 326 4500 800 5300
85.12 25.81 9.82 32.48 0 0 6.89 2.12 1036 4039 213 771 1596 4061 5657
91.24 8.56 23.33 30.61 0 0 6.87 1.93 1190 3823 209 631 1599 4065 5664
23.39 18.12 89.47 0 2.64 22.95 6.86 4.26 952 3600 204 933 3745 806 4551
22.65 20.28 81.53 0 1.71 19.32 6.85 4.23 970 3340 200 572 3745 805 4550
32.92 4.92 86.69 34.47 0 0 6.85 1.92 998 3768 209 639 3600 800 4400
15.93 0 83.33 31.93 5.1 22.06 6.84 0 1020 3780 187 443 3548 803 4351
92.41 27.12 17.83 16.83 0 0 6.83 3.62 952 2478 228 906 1500 4000 5500
0 0 145.89 5.1 2.31 0 6.8 4.91 1384 3624 243 842 4800 0 4800
84.45 25.51 10.08 33.16 0 0 6.8 1.5 1152 3603 217 618 1599 4061 5660
I try to use CSV file as the input of the neural network. But I got the warming as 'could not convert string to float: 'train2.CSV'
' My CSV files contain 15 columns. I have no idea how to convert it to float type. My data is over 10K.
Here is my CSV data looks like
12.71 17.76 91.42 30.78 0 0 6.9 0 978 3576 205 326 4500 800 5300
85.12 25.81 9.82 32.48 0 0 6.89 2.12 1036 4039 213 771 1596 4061 5657
91.24 8.56 23.33 30.61 0 0 6.87 1.93 1190 3823 209 631 1599 4065 5664
23.39 18.12 89.47 0 2.64 22.95 6.86 4.26 952 3600 204 933 3745 806 4551
22.65 20.28 81.53 0 1.71 19.32 6.85 4.23 970 3340 200 572 3745 805 4550
32.92 4.92 86.69 34.47 0 0 6.85 1.92 998 3768 209 639 3600 800 4400
15.93 0 83.33 31.93 5.1 22.06 6.84 0 1020 3780 187 443 3548 803 4351
92.41 27.12 17.83 16.83 0 0 6.83 3.62 952 2478 228 906 1500 4000 5500
0 0 145.89 5.1 2.31 0 6.8 4.91 1384 3624 243 842 4800 0 4800
84.45 25.51 10.08 33.16 0 0 6.8 1.5 1152 3603 217 618 1599 4061 5660
import tensorflow as tf #from tensorflow.examples.tutorials.mnist import input_data import numpy as np train_x,train_y,test_x,test_y = ('train2.CSV','trainY.CSV','test2.CSV.','Text Y.CSV.') n_nodes_hl1 = 500 n_nodes_hl2 = 500 n_nodes_hl3 = 500 n_classes = 2 batch_size = 100 hm_epochs = 10 x = tf.placeholder('float') y = tf.placeholder('float') hidden_1_layer = {'f_fum':n_nodes_hl1, 'weight':tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])), 'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))} hidden_2_layer = {'f_fum':n_nodes_hl2, 'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))} hidden_3_layer = {'f_fum':n_nodes_hl3, 'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])), 'bias':tf.Variable(tf.random_normal([n_nodes_hl3]))} output_layer = {'f_fum':None, 'weight':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])), 'bias':tf.Variable(tf.random_normal([n_classes])),} # Nothing changes def neural_network_model(data): l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias']) l1 = tf.nn.relu(l1) l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias']) l2 = tf.nn.relu(l2) l3 = tf.add(tf.matmul(l2,hidden_3_layer['weight']), hidden_3_layer['bias']) l3 = tf.nn.relu(l3) output = tf.matmul(l3,output_layer['weight']) + output_layer['bias'] return output def train_neural_network(x): prediction = neural_network_model(x) cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y)) #tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) ) an easier, more compact way. optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost) with tf.Session() as sess: sess.run(tf.initialize_all_variables()) for epoch in range(hm_epochs): epoch_loss = 0 i=0 while i < len(train_x): start = i end = i+batch_size batch_x = np.array(train_x[start:end]) batch_y = np.array(train_y[start:end]) _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y}) epoch_loss += c i+=batch_size print('Epoch', epoch+1, 'completed out of',hm_epochs,'loss:',epoch_loss) correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct, 'float')) print('Accuracy:',accuracy.eval({x:test_x, y:test_y})) train_neural_network(x)