I am trying to use cnn-lstm model on this dataset. I've stored this dataset in dataframe named as df. there are totally 11 column in this dataset but i am just mentioning 9 columns here. All columns have numerical values only
Area book_hotel votes location hotel_type Total_Price Facilities Dine rate
6 0 0 1 163 400 22 7 4.4
19 1 2 7 122 220 28 11 4.6
X=df.drop(['rate'],axis=1)
Y=df['rate']
x_train, x_test, y_train, y_test = train_test_split(np.asarray(X), np.asarray(Y), test_size=0.33, shuffle= True)
x_train has shape (3350,10) and
x_test has shape (1650, 10)
# The known number of output classes.
num_classes = 10
# Input image dimensions
input_shape = (10,)
# Convert class vectors to binary class matrices. This uses 1 hot encoding.
y_train_binary = keras.utils.to_categorical(y_train, num_classes)
y_test_binary = keras.utils.to_categorical(y_test, num_classes)
x_train = x_train.reshape(3350, 10,1)
x_test = x_test.reshape(1650, 10,1)
input_layer = Input(shape=(10, 1))
conv1 = Conv1D(filters=32,
kernel_size=8,
strides=1,
activation='relu',
padding='same')(input_layer)
lstm1 = LSTM(32, return_sequences=True)(conv1)
output_layer = Dense(1, activation='sigmoid')(lstm1)
model = Model(inputs=input_layer, outputs=output_layer)
model.summary()
model.compile(loss='mse',optimizer='adam')
Finally when i am trying to fit the model with input
model.fit(x_train,y_train)
ValueError Traceback (most recent call last)
<ipython-input-170-4719cf73997a> in <module>()
----> 1 model.fit(x_train,y_train)
2 frames
/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
133 ': expected ' + names[i] + ' to have ' +
134 str(len(shape)) + ' dimensions, but got array '
--> 135 'with shape ' + str(data_shape))
136 if not check_batch_axis:
137 data_shape = data_shape[1:]
ValueError: Error when checking target: expected dense_2 to have 3 dimensions, but got array with shape (3350, 1)
Can someone please help me resolving this error
I see some problem in your code...
the last dimension output must be equal to the number of class and with multiclass tasks you need to apply a softmax activation: Dense(num_classes, activation='softmax')
you must set return_sequences=False in your last lstm cell because you need a 2D output and not a 3D
you must use categorical_crossentropy as loss function with one-hot encoded target
here a complete dummy example...
num_classes = 10
n_sample = 1000
X = np.random.uniform(0,1, (n_sample,10,1))
y = tf.keras.utils.to_categorical(np.random.randint(0,num_classes, n_sample))
input_layer = Input(shape=(10, 1))
conv1 = Conv1D(filters=32,
kernel_size=8,
strides=1,
activation='relu',
padding='same')(input_layer)
lstm1 = LSTM(32, return_sequences=False)(conv1)
output_layer = Dense(num_classes, activation='softmax')(lstm1)
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(loss='categorical_crossentropy',optimizer='adam')
model.fit(X,y, epochs=5)
Related
I'm new to pytorch and wrote a simple code as following to classify some inputs. The model input has 8*2 with batch size of 2 and the input layer in the model has 2 nodes. I don't know what is wrong!
X1=np.array([[2,1],[3,2],[-4,-1],[-1,-3],[2,-1],[3,-3],[-2,1],[-4,-2]])
Y1=np.array([0,0,0,0,1,1,1,1])
X=torch.tensor(X1)
Y=torch.tensor(Y1)
BATCH_SIZE=2
trainset= torch.utils.data.TensorDataset(X, Y)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,
shuffle=True, num_workers=1)
from torch.nn.modules import flatten
learning_rate = 0.01
num_epochs = 20
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = MyModel()
model = model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
## compute accuracy
def get_accuracy(logit, target, batch_size):
''' Obtain accuracy for training round '''
corrects = (torch.max(logit, 1)[1].view(target.size()).data == target.data).sum()
accuracy = 100.0 * corrects/batch_size
return accuracy.item()
model = MyModel()
# Commented out IPython magic to ensure Python compatibility.
for epoch in range(num_epochs):
train_running_loss = 0.0
train_acc = 0.0
## training step
for inputs, labels in trainloader:
#inputs=torch.flatten(inputs)
inputs,labels=inputs.to(device), labels.to(device)
#inputs = inputs.to(device)
#labels = labels.to(device)
optimizer.zero_grad()
## forward + backprop + loss
print(inputs)
outputs = model.forward(inputs)
loss = criterion(outputs, labels)
loss.backward()
## update model params
optimizer.step()
train_running_loss += loss.detach().item()
train_acc += get_accuracy(outputs, labels, BATCH_SIZE)
#model.train()
model.eval()
print('Epoch: %d | Loss: %.4f | Train Accuracy: %.2f'%(epoch, train_running_loss / i, train_acc/i))
And my model is as below:
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.d1 = nn.Linear(2,3)
self.d2 = nn.Linear(3,1)
self.init_weights()
def init_weights(self):
k1=torch.tensor([0.1,-0.72,0.94,-0.29,0.12,0.44])
k1=torch.unsqueeze(torch.unsqueeze(k1,0),0)
self.d1.weight.data=k1
k2=torch.tensor([1,-1.16,-0.26])
k2=torch.unsqueeze(torch.unsqueeze(k2,0),0)
self.d2.weight.data=k2
def forward(self, x):
x = self.d1(x)
x = F.tanh(x)
x = self.d2(x)
out = F.sigmoid(x)
return out
Then I got an error:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-27-196d819d3ccd> in <module>
101 print(inputs)
102
--> 103 outputs = model.forward(inputs)
104 loss = criterion(outputs, labels)
105
2 frames
/usr/local/lib/python3.8/dist-packages/torch/nn/modules/linear.py in forward(self, input)
112
113 def forward(self, input: Tensor) -> Tensor:
--> 114 return F.linear(input, self.weight, self.bias)
115
116 def extra_repr(self) -> str:
RuntimeError: t() expects a tensor with <= 2 dimensions, but self is 3D
I flatten the input but nothing changed. What should I do to fix it?
First of all, you don't need to invoke your model's forward pass by model.forward(x); using model(x) is good.
Second of all, what exactly are you trying to achieve via the init_weights method? You're unsqueezing k1 and k2 twice, giving them the shape of (1, 1, x) which is 3D which is what the error is telling you. torch.nn.Linear performs a matrix multiplication with a 2D matrix, so you can't use a 3D one. torch.nn.Linear already initializes the weights via Kaiming initialization [1] so I'm not sure what you're trying to achieve here.
Changing the init_weights method to:
def init_weights(self):
k1 = torch.tensor([0.1, -0.72, 0.94, -0.29, 0.12, 0.44])
k1 = k1.reshape(self.d1.weight.shape)
self.d1.weight.data = k1
k2 = torch.tensor([1, -1.16, -0.26])
k2 = k2.reshape(self.d2.weight.shape)
self.d2.weight.data = k2
and changing the type of inputs from Long to Float (i.e., model(inputs.float())) should solve your problem.
References
[1] https://github.com/pytorch/pytorch/blob/0dceaf07cd1236859953b6f85a61dc4411d10f87/torch/nn/modules/linear.py#L103
I am new to ML and have been trying Feature selection with RFE approach. My dataset has 5K records and its binary classification problem. This is the code that I am following based on a tutorial online
#no of features
nof_list=np.arange(1,13)
high_score=0
#Variable to store the optimum features
nof=0
score_list =[]
for n in range(len(nof_list)):
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state = 0)
model = RandomForestClassifier()
rfe = RFE(model,nof_list[n])
X_train_rfe = rfe.fit_transform(X_train,y_train)
X_test_rfe = rfe.transform(X_test)
model.fit(X_train_rfe,y_train)
score = model.score(X_test_rfe,y_test)
score_list.append(score)
if(score>high_score):
high_score = score
nof = nof_list[n]
print("Optimum number of features: %d" %nof)
print("Score with %d features: %f" % (nof, high_score))
I encounter the below error. Can someone please help
TypeError Traceback (most recent call last)
<ipython-input-332-a23dfb331001> in <module>
9 model = RandomForestClassifier()
10 rfe = RFE(model,nof_list[n])
---> 11 X_train_rfe = rfe.fit_transform(X_train,y_train)
12 X_test_rfe = rfe.transform(X_test)
13 model.fit(X_train_rfe,y_train)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
554 Training set.
555
--> 556 y : numpy array of shape [n_samples]
557 Target values.
558
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\feature_selection\_base.py in transform(self, X)
75 X = check_array(X, dtype=None, accept_sparse='csr',
76 force_all_finite=not tags.get('allow_nan', True))
---> 77 mask = self.get_support()
78 if not mask.any():
79 warn("No features were selected: either the data is"
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\feature_selection\_base.py in get_support(self, indices)
44 values are indices into the input feature vector.
45 """
---> 46 mask = self._get_support_mask()
47 return mask if not indices else np.where(mask)[0]
48
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\feature_selection\_rfe.py in _get_support_mask(self)
269
270 def _get_support_mask(self):
--> 271 check_is_fitted(self)
272 return self.support_
273
TypeError: check_is_fitted() missing 1 required positional argument: 'attributes'
What is your sklearn version ?
The following (using artificial data) should work fine:
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.feature_selection import RFE
from sklearn.ensemble import RandomForestClassifier
X = np.random.rand(100,20)
y = np.ones((X.shape[0]))
#no of features
nof_list=np.arange(1,13)
high_score=0
#Variable to store the optimum features
nof=0
score_list =[]
for n in range(len(nof_list)):
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state = 0)
model = RandomForestClassifier()
rfe = RFE(model,nof_list[n])
X_train_rfe = rfe.fit_transform(X_train,y_train)
X_test_rfe = rfe.transform(X_test)
model.fit(X_train_rfe,y_train)
score = model.score(X_test_rfe,y_test)
score_list.append(score)
if(score>high_score):
high_score = score
nof = nof_list[n]
print("Optimum number of features: %d" %nof)
print("Score with %d features: %f" % (nof, high_score))
Optimum number of features: 1
Score with 1 features: 1.000000
Versions tested:
sklearn.__version__
'0.20.4'
sklearn.__version__
'0.21.3'
Here is the network diagram I am working on and data is tabular and structured,
On the left, we have some abilities which are continuous features and on the right, we could have 'N' number of modifiers. Each modifier has modifier_type which is categorical and some statistics which are continuous features.
If it was only one modifier here is the code that works just fine!
import keras.backend as K
from keras.models import Model
from keras.layers import Input, Embedding, concatenate
from keras.layers import Dense, GlobalMaxPooling1D, Reshape
from keras.optimizers import Adam
K.clear_session()
# Using embeddings for categorical features
modifier_type_embedding_in=[]
modifier_type_embedding_out=[]
# sample categorical features
categorical_features = ['modifier_type']
modifier_input_ = Input(shape=(1,), name='modifier_type_in')
# Let's assume 10 unique type of modifiers and let's have embedding dimension as 6
modifier_output_ = Embedding(input_dim=10, output_dim=6, name='modifier_type')(modifier_input_)
modifier_output_ = Reshape(target_shape=(6,))(modifier_output_)
modifier_type_embedding_in.append(modifier_input_)
modifier_type_embedding_out.append(modifier_output_)
# sample continuous features
statistics = ['duration']
statistics_inputs =[Input(shape=(len(statistics),), name='statistics')] # Input(shape=(1,))
# sample continuous features
abilities = ['buyback_cost', 'cooldown', 'number_of_deaths', 'ability', 'teleport', 'team', 'level', 'max_mana', 'intelligence']
abilities_inputs=[Input(shape=(len(abilities),), name='abilities')] # Input(shape=(9,))
concat = concatenate(modifier_type_embedding_out + statistics_inputs)
FC_relu = Dense(128, activation='relu', name='fc_relu_1')(concat)
FC_relu = Dense(128, activation='relu', name='fc_relu_2')(FC_relu)
model = concatenate(abilities_inputs + [FC_relu])
model = Dense(64, activation='relu', name='fc_relu_3')(model)
model_out = Dense(1, activation='sigmoid', name='fc_sigmoid')(model)
model_in = abilities_inputs + modifier_type_embedding_in + statistics_inputs
model = Model(inputs=model_in, outputs=model_out)
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=2e-05, decay=1e-3), metrics=['accuracy'])
However, while compiling for 'N' number of modifiers I get below error and below are the changes I made in code,
modifier_input_ = Input(shape=(None, 1,), name='modifier_type_in')
statistics_inputs =[Input(shape=(None, len(statistics),), name='statistics')] # Input(shape=(None, 1,))
FC_relu = Dense(128, activation='relu', name='fc_relu_2')(FC_relu)
max_pool = GlobalMaxPooling1D()(FC_relu)
model = concatenate(abilities_inputs + [max_pool])
Here is what I get,
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-7703088b1d24> in <module>
22 abilities_inputs=[Input(shape=(len(abilities),), name='abilities')] # Input(shape=(9,))
23
---> 24 concat = concatenate(modifier_type_embedding_out + statistics_inputs)
25 FC_relu = Dense(128, activation='relu', name='fc_relu_1')(concat)
26 FC_relu = Dense(128, activation='relu', name='fc_relu_2')(FC_relu)
e:\Miniconda3\lib\site-packages\keras\layers\merge.py in concatenate(inputs, axis, **kwargs)
647 A tensor, the concatenation of the inputs alongside axis `axis`.
648 """
--> 649 return Concatenate(axis=axis, **kwargs)(inputs)
650
651
e:\Miniconda3\lib\site-packages\keras\engine\base_layer.py in __call__(self, inputs, **kwargs)
423 'You can build it manually via: '
424 '`layer.build(batch_input_shape)`')
--> 425 self.build(unpack_singleton(input_shapes))
426 self.built = True
427
e:\Miniconda3\lib\site-packages\keras\layers\merge.py in build(self, input_shape)
360 'inputs with matching shapes '
361 'except for the concat axis. '
--> 362 'Got inputs shapes: %s' % (input_shape))
363
364 def _merge_function(self, inputs):
ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 6), (None, None, 1)]
How do I use the embedding layer within a neural network designed to accept variable input length features?
The answer is,
import keras.backend as K
from keras.models import Model
from keras.layers import Input, Embedding, concatenate
from keras.layers import Dense, GlobalMaxPooling1D, Reshape
from keras.optimizers import Adam
K.clear_session()
# Using embeddings for categorical features
modifier_type_embedding_in=[]
modifier_type_embedding_out=[]
# sample categorical features
categorical_features = ['modifier_type']
modifier_input_ = Input(shape=(None,), name='modifier_type_in')
# Let's assume 10 unique type of modifiers and let's have embedding dimension as 6
modifier_output_ = Embedding(input_dim=10, output_dim=6, name='modifier_type')(modifier_input_)
modifier_type_embedding_in.append(modifier_input_)
modifier_type_embedding_out.append(modifier_output_)
# sample continuous features
statistics = ['duration']
statistics_inputs =[Input(shape=(None, len(statistics),), name='statistics')] # Input(shape=(1,))
# sample continuous features
abilities = ['buyback_cost', 'cooldown', 'number_of_deaths', 'ability', 'teleport', 'team', 'level', 'max_mana', 'intelligence']
abilities_inputs=[Input(shape=(len(abilities),), name='abilities')] # Input(shape=(9,))
concat = concatenate(modifier_type_embedding_out + statistics_inputs)
FC_relu = Dense(128, activation='relu', name='fc_relu_1')(concat)
FC_relu = Dense(128, activation='relu', name='fc_relu_2')(FC_relu)
max_pool = GlobalMaxPooling1D()(FC_relu)
model = concatenate(abilities_inputs + [max_pool])
model = Dense(64, activation='relu', name='fc_relu_3')(model)
model_out = Dense(1, activation='sigmoid', name='fc_sigmoid')(model)
model_in = abilities_inputs + modifier_type_embedding_in + statistics_inputs
model = Model(inputs=model_in, outputs=model_out)
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=2e-05, decay=1e-3), metrics=['accuracy'])
Because of the large data, I'm using fit_generator with a custom generator to train LSTM model.
I haven't used LSTM with fit_generator before, so I don't know whether my code is correct.
def generator_v2(trainDir,nb_classes,batch_size):
print('start generator')
classes = ["G11","G15","G17","G19","G32","G34","G48","G49"]
while 1:
print('loop generator')
for root, subdirs, files in os.walk(trainDir):
for file in files:
try:
label = root.split("\\")[-1]
label = classes.index(label)
label = to_categorical(label,num_classes=nb_classes).reshape(1,nb_classes)
df = pd.read_csv(root +"\\"+ file)
batches = int(np.ceil(len(df) / batch_size))
for i in range(0, batches):
x_batch = df[i * batch_size:min(len(df), i * batch_size + batch_size)].values
x_batch = x_batch.reshape(1, x_batch.shape[0], x_batch.shape[1])
yield x_batch, label
del df
except EOFError:
print("error" + file)
trainDir = "data_diff_level2_statistics"
nb_classes = 8
batch_size = 128
MaxLen = 449 # each csv file has 449 rows,
batches = int(np.ceil(MaxLen / batch_size))
filesCount = sum([len(files) for r, d, files in os.walk(trainDir)]) # the number of all files
steps_per_epoch = batches*filesCount
model = Sequential()
model.add(LSTM(4,input_shape=(None,5)))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adadelta',metrics=['acc'])
model.fit_generator(generator_v2(trainDir,nb_classes,batch_size),steps_per_epoch=steps_per_epoch, nb_epoch = 100, verbose=1)
Do I set the correct number of steps_per_epoch?
My all training data shape is : (230,449,5)
So, I set steps_per_epoch with 230 * (449/batch_size).
(449/batch_size) means I read a csv file 128 rows at a time.
The argument steps_per_epoch should be equal to the total number of samples (length of your training set) divided by batch_size(the same is available for validation_steps.
In your example, the length of the dataset is given by dataset_length = number_of_csv_files * length_of_csv_file.
Therefore, your code is correct since you have 230 * (449/batch_size), which is similar to what I have written above.
I have issue with constructing Input data for BiRNN network.
I'm create License Plate detection system like described : https://arxiv.org/pdf/1601.05610v1.pdf
I have got to "4.2.3 Sequence Labelling" part where I need to train BiRNN with dataset of (total_count_of_images, None, 256) shape, None because it's length of image and and it is different for every picture in data set.
Let's say I have 3000 Images. Then shape would look like :
train.shape : (3000,) but really it is (3000, None, 256) !?
So I got example code from
https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb
So I'm struggling even with starting to train my RNN. I don't understand how I need to constrct input data/model, input placeholders, variables etc to achieve any training process.
As far as I know everything should work. My code :
reset_graph()
'''
Dataset : (10000, 784)
Labels : (10000, 10)
To classify images using a bidirectional reccurent neural network, we consider
every image row as a sequence of pixels. Because MNIST image shape is 28*28px,
we will then handle 28 sequences of 28 steps for every sample.
'''
# Parameters
learning_rate = 0.001
training_iters = 100 # 100000
display_step = 10
batch_size = 40
# Network Parameters
n_input = 256 # data inpit size/256D
n_steps = 256 # timesteps
n_hidden = 200 # hidden layer num of features
n_classes = 36 # MNIST total classes (0-9 digits and a-z letters)
# tf Graph input
x = tf.placeholder("float", [batch_size, None , n_input], name='input_placeholder')
y = tf.placeholder("float", [batch_size, None, n_classes], name='labels_placeholder')
# Define weights
weights = {
# Hidden layer weights => 2*n_hidden because of foward + backward cells
'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))
}
biases = {
'out': tf.Variable(tf.random_normal([n_classes]))
}
def BiRNN(x, weights, biases):
print('Input x',x.get_shape().as_list())
print('weights[\'out\']', weights['out'].get_shape().as_list())
print('biases[\'out\']', biases['out'].get_shape().as_list())
# Prepare data shape to match `bidirectional_rnn` function requirements
# Current data input shape: (batch_size, n_steps, n_input)
# Required shape: 'n_steps' tensors list of shape (batch_size, n_input)
# Permuting batch_size and n_steps
#x = tf.transpose(x, [1, 0, 2])
#print('Transposed x',x.get_shape().as_list())
# Reshape to (n_steps*batch_size, n_input)
x = tf.reshape(x, [-1, n_steps])
print('Reshaped x',x.get_shape().as_list())
# Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)
x = tf.split(0, n_input, x)
print(len(x),'of [ ',x[0],' ] kinds')
# Define lstm cells with tensorflow
# Forward direction cell
lstm_fw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
# Backward direction cell
lstm_bw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
# Get lstm cell output
outputs, _, _ = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32)
print( len(outputs),'of [ ',outputs[0],' ] kinds' )
# Linear activation, using rnn inner loop last output
ret = tf.matmul(outputs[-1], weights['out']) + biases['out']
print('ret', ret.get_shape().as_list())
return ret
pred = BiRNN(x, weights, biases)
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Evaluate model
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.initialize_all_variables()
OUTPUT :
Input x [40, None, 256]
weights['out'] [400, 36]
biases['out'] [36]
Reshaped x [None, 256]
256 of [ Tensor("split:0", shape=(?, 256), dtype=float32) ] kinds
256 of [ Tensor("concat:0", shape=(?, 400), dtype=float32) ] kinds
ret [None, 36]
Everything just right there.
Problems start at session part :
# Launch the graph
with tf.Session() as sess:
sess.run(init)
step = 1
batch_data = batch_gen(batch_size)
# Keep training until reach max iterations
while step * batch_size < training_iters:
batch_x, batch_y = next(batch_data)
print(batch_x.shape)
print(batch_y.shape)
#m[:,0, None, None].shape
#Run optimization op (backprop)
print('Optimizer')
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
if step % display_step == 0:
print('Display')
# Calculate batch accuracy
acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
# Calculate batch loss
loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
print("Iter " + str(step * batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
step += 1
print("Optimization Finished!")
# Calculate accuracy for 128 mnist test images
test_len = 128
test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))
test_label = mnist.test.labels[:test_len]
print("Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: test_data, y: test_label}))
There I got following error :
(40,)
(40,)
Optimizer
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-96-a53814db8181> in <module>()
14 #Run optimization op (backprop)
15 print('Optimizer')
---> 16 sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
17
18 if step % display_step == 0:
/home/nauris/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
715 try:
716 result = self._run(None, fetches, feed_dict, options_ptr,
--> 717 run_metadata_ptr)
718 if run_metadata:
719 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/home/nauris/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
886 ' to a larger type (e.g. int64).')
887
--> 888 np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
889
890 if not subfeed_t.get_shape().is_compatible_with(np_val.shape):
/home/nauris/anaconda3/lib/python3.5/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
480
481 """
--> 482 return array(a, dtype, copy=False, order=order)
483
484 def asanyarray(a, dtype=None, order=None):
ValueError: setting an array element with a sequence.
Any help would be highly appreciated. Thanks everyone in advance.
Realized that error occurs because you cannot feed numpy ndarray with inconsistent dimensions such as (3000, None, 256) in my case. Haven't found any solution yet.