Nsryjdtyk

I am using Tensorflow to train my data for gesture recognition. Here is my Code:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from scipy import stats

import tensorflow as tf



%matplotlib inline

plt.style.use('ggplot')



def read_data(file_path):

    column_names = ['user-id','activity','timestamp','x-axis','y-axis','z-axis']

    data = pd.read_csv(file_path,header = None, names = column_names, comment=';')

    return data



def feature_normalize(dataset):

    mu = np.mean(dataset,axis = 0)

    sigma = np.std(dataset,axis = 0)

    return (dataset - mu)/sigma



def plot_axis(ax, x, y, title):

    ax.plot(x, y)

    ax.set_title(title)

    ax.xaxis.set_visible(False)

    ax.set_ylim([min(y) - np.std(y), max(y) + np.std(y)])

    ax.set_xlim([min(x), max(x)])

    ax.grid(True)



def plot_activity(activity,data):

    fig, (ax0, ax1, ax2) = plt.subplots(nrows = 3, figsize = (15, 10), sharex = True)

    plot_axis(ax0, data['timestamp'], data['x-axis'], 'x-axis')

    plot_axis(ax1, data['timestamp'], data['y-axis'], 'y-axis')

    plot_axis(ax2, data['timestamp'], data['z-axis'], 'z-axis')

    plt.subplots_adjust(hspace=0.2)

    fig.suptitle(activity)

    plt.subplots_adjust(top=0.90)

    plt.show()



dataset = read_data('C:\Users\ASUS\Desktop\final_data.txt')

dataset['x-axis'] = feature_normalize(dataset['x-axis'])

dataset['y-axis'] = feature_normalize(dataset['y-axis'])

dataset['z-axis'] = feature_normalize(dataset['z-axis'])



for activity in np.unique(dataset["activity"]):

    subset = dataset[dataset["activity"] == activity][:34]

    plot_activity(activity,subset)



def windows(data, size):

    start = 0

    while start < data.count():

        yield int(start), int(start + size)

        start += (size / 2)



def segment_signal(data,window_size = 17):

    segments = np.empty((0,window_size,3))

    labels = np.empty((0))

    for (start, end) in windows(data["timestamp"], window_size):

        x = data["x-axis"][start:end]

        y = data["y-axis"][start:end]

        z = data["z-axis"][start:end]

        if(len(dataset["timestamp"][start:end]) == window_size):

            segments = np.vstack([segments,np.dstack([x,y,z])])

            labels = np.append(labels,stats.mode(data["activity"][start:end])[0][0])

    return segments, labels



segments, labels = segment_signal(dataset)

labels = np.asarray(pd.get_dummies(labels), dtype = np.int8)

reshaped_segments = segments.reshape(len(segments), 1,17, 3)



train_test_split = np.random.rand(len(reshaped_segments)) < 0.80

train_x = reshaped_segments[train_test_split]

train_y = labels[train_test_split]

test_x = reshaped_segments[~train_test_split]

test_y = labels[~train_test_split]





input_height = 1

input_width = 17

num_labels = 7

num_channels = 3



batch_size = 100

kernel_size = 5

depth = 60

num_hidden = 1000



learning_rate = 0.0001

training_epochs = 10



total_batchs = train_x.shape[0] // batch_size



def weight_variable(shape):

    initial = tf.truncated_normal(shape, stddev = 0.1)

    return tf.Variable(initial)



def bias_variable(shape):

    initial = tf.constant(0.0, shape = shape)

    return tf.Variable(initial)



def depthwise_conv2d(x, W):

    return tf.nn.depthwise_conv2d(x,W, [1, 1, 1, 1], padding='VALID')



def apply_depthwise_conv(x,kernel_size,num_channels,depth):

    weights = weight_variable([1, kernel_size, num_channels, depth])

    biases = bias_variable([depth * num_channels])

    return tf.nn.relu(tf.add(depthwise_conv2d(x, weights),biases))



def apply_max_pool(x,kernel_size,stride_size):

    return tf.nn.max_pool(x, ksize=[1, 1, kernel_size, 1], 

                          strides=[1, 1, stride_size, 1], padding='VALID')



X = tf.placeholder(tf.float32, shape=[None,input_height,input_width,num_channels],name="Mul")

Y = tf.placeholder(tf.float32, shape=[None,num_labels])



c = apply_depthwise_conv(X,kernel_size,num_channels,depth)

p = apply_max_pool(c,4,2)

c = apply_depthwise_conv(p,3,depth*num_channels,depth//6)



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//6), num_hidden]) #10

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))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))



saver = tf.train.Saver()

with tf.Session() as session:

    tf.global_variables_initializer().run()

    for epoch in range(training_epochs):

        cost_history = np.empty(shape=[1],dtype=float)

        for b in range(total_batchs):    

            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}))

    tf.train.write_graph(session.graph_def, '.', '../har.pbtxt')  

    saver.save(session,save_path = "../har.ckpt")

In the last line encounter this error message

ValueError: Cannot feed value of shape (10, 9) for Tensor 'Placeholder_2:0', which has shape '(?, 7)'

I tried changing some values but still I cannot fix this error.

What can I do to resolve this error?