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ValueError: Error when checking input: expected gru_5_input to have shape (None, None, 10) but got array with shape (1, 4, 1)

I am trying to make hourly predictions using a recurrent neural network using TensorFlow and Keras in Python.I have assigned my inputs of the neural network to be (None, None, 5) shown in my .

However, I am getting the errorː

ValueError: Error when checking input: expected gru_3_input to have shape (None, None, 10) but got array with shape (1, 4, 1) My MVCE code isː

ValueError: Error when checking input: expected gru_3_input to have shape (None, None, 10) but got array with shape (1, 4, 1)

%matplotlib inline
#!pip uninstall keras
#!pip install keras==2.1.2
import tensorflow as tf
import pandas as pd
from pandas import DataFrame
import math
import numpy
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
import datetime
from keras.layers import Input, Dense, GRU, Embedding
from keras.optimizers import RMSprop
from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau


datetime = [datetime.datetime(2012, 1, 1, 1, 0, 0) + datetime.timedelta(hours=i) for i in range(10)]




X=np.array([2.25226244,1.44078451,0.99174488,0.71179491,0.92824542,1.67776948,2.96399534,5.06257161,7.06504245,7.77817664
,0.92824542,1.67776948,2.96399534,5.06257161,7.06504245,7.77817664])

y= np.array([0.02062136,0.00186715,0.01517354,0.0129046 ,0.02231125,0.01492537,0.09646542,0.28444476,0.46289928,0.77817664
,0.02231125,0.01492537,0.09646542,0.28444476,0.46289928,0.77817664])

X = X[1:11]
y= y[1:11]

df = pd.DataFrame({'date':datetime,'y':y,'X':X})



df['t']= [x for x in range(10)]
df['X-1'] = df['X'].shift(-1)


x_data = df['X-1'].fillna(0)

y_data = y

num_data = len(x_data)

#### training and testing split####
train_split = 0.6
num_train = int(train_split*num_data)
num_test = num_data-num_train## number of observations in test set


#input train test
x_train = x_data[0:num_train].reshape(-1, 1)
x_test = x_data[num_train:].reshape(-1, 1)
#print (len(x_train) +len( x_test))

#output train test
y_train = y_data[0:num_train].reshape(-1, 1)
y_test = y_data[num_train:].reshape(-1, 1)
#print (len(y_train) + len(y_test))


### number of input signals
num_x_signals = x_data.shape[0]
# print (num_x_signals)

## number of output signals##

num_y_signals = y_data.shape[0]
#print (num_y_signals)

####data scalling'###

x_scaler = MinMaxScaler(feature_range=(0,1))
x_train_scaled = x_scaler.fit_transform(x_train)



x_test_scaled = MinMaxScaler(feature_range=(0,1)).fit_transform(x_test)

y_scaler = MinMaxScaler()
y_train_scaled = y_scaler.fit_transform(y_train)
y_test_scaled = MinMaxScaler(feature_range=(0,1)).fit_transform(y_test)



def batch_generator(batch_size, sequence_length):
"""
Generator function for creating random batches of training-data.
"""

# Infinite loop. providing the neural network with random data from the
# datase for x and y
while True:
# Allocate a new array for the batch of input-signals.
x_shape = (batch_size, sequence_length, num_x_signals)
x_batch = np.zeros(shape=x_shape, dtype=np.float16)

# Allocate a new array for the batch of output-signals.
y_shape = (batch_size, sequence_length, num_y_signals)
y_batch = np.zeros(shape=y_shape, dtype=np.float16)

# Fill the batch with random sequences of data.
for i in range(batch_size):
# Get a random start-index.
# This points somewhere into the training-data.
idx = np.random.randint(num_train - sequence_length)

# Copy the sequences of data starting at this index.
x_batch[i] = x_train_scaled[idx:idx+sequence_length]
y_batch[i] = y_train_scaled[idx:idx+sequence_length]

yield (x_batch, y_batch)


batch_size =20




sequence_length = 2


generator = batch_generator(batch_size=batch_size,
sequence_length=sequence_length)

x_batch, y_batch = next(generator)




#########Validation Set########

validation_data = (np.expand_dims(x_test_scaled, axis=0),
np.expand_dims(y_test_scaled, axis=0))


#####Create the Recurrent Neural Network###


model = Sequential()


model.add(GRU(units=5,
return_sequences=True,
input_shape=(None, num_x_signals)))

## This line is going to map the above 512 values to just 1 (num_y_signal)
model.add(Dense(num_y_signals, activation='sigmoid'))

if False:
from tensorflow.python.keras.initializers import RandomUniform

# Maybe use lower init-ranges.##### I may have to change these during debugging####
init = RandomUniform(minval=-0.05, maxval=0.05)

model.add(Dense(num_y_signals,
activation='linear',
kernel_initializer=init))

warmup_steps = 5

def loss_mse_warmup(y_true, y_pred):

#
# Ignore the "warmup" parts of the sequences
# by taking slices of the tensors.
y_true_slice = y_true[:, warmup_steps:, :]
y_pred_slice = y_pred[:, warmup_steps:, :]

# These sliced tensors both have this shape:
# [batch_size, sequence_length - warmup_steps, num_y_signals]

# Calculate the MSE loss for each value in these tensors.
# This outputs a 3-rank tensor of the same shape.
loss = tf.losses.mean_squared_error(labels=y_true_slice,
predictions=y_pred_slice)



loss_mean = tf.reduce_mean(loss)

return loss_mean

optimizer = RMSprop(lr=1e-3) ### This is somthing related to debugging



model.compile(loss=loss_mse_warmup, optimizer=optimizer)#### I may have to make the output a singnal rather than the whole data set

print(model.summary())


model.fit_generator(generator=generator,
epochs=20,
steps_per_epoch=100,
validation_data=validation_data)


I am not sure why this could be, but i believe it could something to do with reshaping my training and testing data. ɪ have also attached my full error message to my code to make the problem reproducible.

validation_data

(1,4,1)

1 Answer
1

I'm unsure about the correctness but here it is:

%matplotlib inline
#!pip uninstall keras
#!pip install keras==2.1.2
import tensorflow as tf
import pandas as pd
from pandas import DataFrame
import math
import numpy
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
import datetime
from keras.layers import Input, Dense, GRU, Embedding
from keras.optimizers import RMSprop
from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau


datetime = [datetime.datetime(2012, 1, 1, 1, 0, 0) + datetime.timedelta(hours=i) for i in range(10)]




X=np.array([2.25226244,1.44078451,0.99174488,0.71179491,0.92824542,1.67776948,2.96399534,5.06257161,7.06504245,7.77817664
,0.92824542,1.67776948,2.96399534,5.06257161,7.06504245,7.77817664])

y= np.array([0.02062136,0.00186715,0.01517354,0.0129046 ,0.02231125,0.01492537,0.09646542,0.28444476,0.46289928,0.77817664
,0.02231125,0.01492537,0.09646542,0.28444476,0.46289928,0.77817664])

X = X[1:11]
y= y[1:11]

df = pd.DataFrame({'date':datetime,'y':y,'X':X})



df['t']= [x for x in range(10)]
df['X-1'] = df['X'].shift(-1)


x_data = df['X-1'].fillna(0)

y_data = y

num_data = len(x_data)

#### training and testing split####
train_split = 0.6
num_train = int(train_split*num_data)
num_test = num_data-num_train## number of observations in test set


#input train test
x_train = x_data[0:num_train].reshape(-1, 1)
x_test = x_data[num_train:].reshape(-1, 1)
#print (len(x_train) +len( x_test))

#output train test
y_train = y_data[0:num_train].reshape(-1, 1)
y_test = y_data[num_train:].reshape(-1, 1)
#print (len(y_train) + len(y_test))


### number of input signals
num_x_signals = x_data.shape[0]
# print (num_x_signals)

## number of output signals##

num_y_signals = y_data.shape[0]
#print (num_y_signals)

####data scalling'###

x_scaler = MinMaxScaler(feature_range=(0,1))
x_train_scaled = x_scaler.fit_transform(x_train)



x_test_scaled = MinMaxScaler(feature_range=(0,1)).fit_transform(x_test)

y_scaler = MinMaxScaler()
y_train_scaled = y_scaler.fit_transform(y_train)
y_test_scaled = MinMaxScaler(feature_range=(0,1)).fit_transform(y_test)



def batch_generator(batch_size, sequence_length):
"""
Generator function for creating random batches of training-data.
"""

# Infinite loop. providing the neural network with random data from the
# datase for x and y
while True:
# Allocate a new array for the batch of input-signals.
x_shape = (batch_size, sequence_length, num_x_signals)
x_batch = np.zeros(shape=x_shape, dtype=np.float16)

# Allocate a new array for the batch of output-signals.
y_shape = (batch_size, sequence_length, num_y_signals)
y_batch = np.zeros(shape=y_shape, dtype=np.float16)

# Fill the batch with random sequences of data.
for i in range(batch_size):
# Get a random start-index.
# This points somewhere into the training-data.
idx = np.random.randint(num_train - sequence_length)

# Copy the sequences of data starting at this index.
x_batch[i] = x_train_scaled[idx:idx+sequence_length]
y_batch[i] = y_train_scaled[idx:idx+sequence_length]

yield (x_batch, y_batch)


batch_size =20




sequence_length = 2


generator = batch_generator(batch_size=batch_size,
sequence_length=sequence_length)

x_batch, y_batch = next(generator)




#########Validation Set Start########

def batch_generator(batch_size, sequence_length):
"""
Generator function for creating random batches of training-data.
"""

# Infinite loop. providing the neural network with random data from the
# datase for x and y
while True:
# Allocate a new array for the batch of input-signals.
x_shape = (batch_size, sequence_length, num_x_signals)
x_batch = np.zeros(shape=x_shape, dtype=np.float16)

# Allocate a new array for the batch of output-signals.
y_shape = (batch_size, sequence_length, num_y_signals)
y_batch = np.zeros(shape=y_shape, dtype=np.float16)

# Fill the batch with random sequences of data.
for i in range(batch_size):
# Get a random start-index.
# This points somewhere into the training-data.
idx = np.random.randint(num_train - sequence_length)

# Copy the sequences of data starting at this index.
x_batch[i] = x_test_scaled[idx:idx+sequence_length]
y_batch[i] = y_test_scaled[idx:idx+sequence_length]

yield (x_batch, y_batch)
validation_data= next(batch_generator(batch_size,sequence_length))

# validation_data = (np.expand_dims(x_test_scaled, axis=0),
# np.expand_dims(y_test_scaled, axis=0))

#Validation set end

#####Create the Recurrent Neural Network###


model = Sequential()


model.add(GRU(units=5,
return_sequences=True,
input_shape=(None, num_x_signals)))

## This line is going to map the above 512 values to just 1 (num_y_signal)
model.add(Dense(num_y_signals, activation='sigmoid'))

if False:
from tensorflow.python.keras.initializers import RandomUniform

# Maybe use lower init-ranges.##### I may have to change these during debugging####
init = RandomUniform(minval=-0.05, maxval=0.05)

model.add(Dense(num_y_signals,
activation='linear',
kernel_initializer=init))

warmup_steps = 5

def loss_mse_warmup(y_true, y_pred):

#
# Ignore the "warmup" parts of the sequences
# by taking slices of the tensors.
y_true_slice = y_true[:, warmup_steps:, :]
y_pred_slice = y_pred[:, warmup_steps:, :]

# These sliced tensors both have this shape:
# [batch_size, sequence_length - warmup_steps, num_y_signals]

# Calculate the MSE loss for each value in these tensors.
# This outputs a 3-rank tensor of the same shape.
loss = tf.losses.mean_squared_error(labels=y_true_slice,
predictions=y_pred_slice)



loss_mean = tf.reduce_mean(loss)

return loss_mean

optimizer = RMSprop(lr=1e-3) ### This is somthing related to debugging



model.compile(loss=loss_mse_warmup, optimizer=optimizer)#### I may have to make the output a singnal rather than the whole data set

print(model.summary())


model.fit_generator(generator=generator,
epochs=20,
steps_per_epoch=100,
validation_data=validation_data)


I've only changed part of code between validation set start and validation set end.

validation set start

validation set end

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