I am trying to implement tensorflow regression model ,my data shape is train_X=(200,4) and train_Y=(200,). i am getting shape error ,here is my piece of code please can anyone mention where i am doing mistake.
df=pd.read_csv('all.csv')
df=df.drop('Time',axis=1)
print(df.describe()) #to understand the dataset
train_Y=df["power"]
train_X=df.drop('power',axis=1)
train_X=numpy.asarray(train_X)
train_Y=numpy.asarray(train_Y)
n_samples = train_X.shape[0]
tf Graph Input
X = tf.placeholder('float',[None,len(train_X[0])])
Y = tf.placeholder("float")
Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")
Construct a linear model
pred = tf.add(tf.multiply(X, W), b)
Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
Gradient descent
Note, minimize() knows to modify W and b because Variable objects are
trainable=True by default
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
Start training
with tf.Session() as sess:
# Run the initializer
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})
# Display logs per epoch step
if (epoch+1) % display_step == 0:
c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
"W=", sess.run(W), "b=", sess.run(b))
print("Optimization Finished!")
training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
# Graphic display
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()enter code here
