The input element has 3 rows each having 199 columns and the output has 46 rows and 1 column
Input.shape, output.shape
((204563, 3, 199), (204563, 46, 1))
When the input is given the following error is thrown:
from keras.layers import Dense
from keras.models import Sequential
from keras.layers.recurrent import SimpleRNN
model = Sequential()
model.add(SimpleRNN(100, input_shape = (Input.shape[1], Input.shape[2])))
model.add(Dense(output.shape[1], activation = 'softmax'))
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
model.fit(Input, output, epochs = 20, batch_size = 200)
error thrown:
Epoch 1/20
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-134-378dd431cf45> in <module>()
3 model.add(Dense(y_target.shape[1], activation = 'softmax'))
4 model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
----> 5 model.fit(X_input, y_target, epochs = 20, batch_size = 200)
.
.
.
ValueError: Error when checking model target: expected dense_6 to have 2 dimensions, but got array with shape (204563, 46, 1)
Please explain the reason for the problem and possible soution
The problem is that SimpleRNN(100) returns a tensor of shape (204563, 100), hence, the Dense(46) (since output.shape[1]=46) will return a tensor of shape (204563, 46), but your y_target have shape (204563, 46, 1). You need to remove the last dimension with, for example, y_target = np.squeeze(y_target), so that the dimension are consistent
Related
I am trying to create a branched Keras model to output multiple classes (age and gender)
My input X_train and X_test have the shape:
(4000,128,128,3)
and
(1000,128,128,3)
this is my code to create the layers:
from keras.models import Sequential,load_model,Model
from keras.layers import Conv2D,MaxPool2D,MaxPooling2D,Dense,Dropout,BatchNormalization,Flatten,Input
from keras.layers import *
#model creation
# input_shape = (128, 128, 3)
# inputs = Input((input_shape))
input = Input(shape=(128,128,3))
conv1 = Conv2D(32,(3,3),activation="relu")(input)
pool1 = MaxPool2D((2,2))(conv1)
conv2 = Conv2D(64,(3,3),activation="relu")(pool1)
pool2 = MaxPool2D((2,2))(conv2)
conv3 = Conv2D(128,(3,3),activation="relu")(pool2)
pool3 = MaxPool2D((2,2))(conv3)
flt = Flatten()(pool3)
#age
age_l = Dense(128,activation="relu")(flt)
age_l = Dense(64,activation="relu")(age_l)
age_l = Dense(32,activation="relu")(age_l)
age_l = Dense(1,activation="relu")(age_l)
#gender
gender_l = Dense(128,activation="relu")(flt)
gender_l = Dense(80,activation="relu")(gender_l)
gender_l = Dense(64,activation="relu")(gender_l)
gender_l = Dense(32,activation="relu")(gender_l)
gender_l = Dropout(0.5)(gender_l)
gender_l = Dense(2,activation="softmax")(gender_l)
modelA = Model(inputs=input,outputs=[age_l,gender_l])
modelA.compile(loss=['mse', 'sparse_categorical_crossentropy'], optimizer='adam', metrics=['accuracy', 'mae'])
modelA.summary()
however, i keep getting this error:
ValueError Traceback (most recent call last)
Cell In [27], line 1
----> 1 save = modelA.fit(X_train_arr, [y_train, y_train2],
2 validation_data = (X_test, [y_test, y_test2]),
3 epochs = 30)
ValueError: Exception encountered when calling layer "model" " f"(type Functional).
Input 0 of layer "conv2d_1" is incompatible with the layer: expected min_ndim=4, found ndim=2. Full shape received: (None, 1)
Call arguments received by layer "model" " f"(type Functional):
• inputs=tf.Tensor(shape=(None, 1), dtype=string)
• training=False
• mask=None
I cannot see what the issue is as the input dimensions seem to be correct?
Apologies I have tried studying similar posts and the required text but still do not understand what the issue is!
I have checked your code and run it with some dummy data, here at my end it is running fine, I think the problem is with your dataset, kindly check your dataset before passing it to the model.
I am trying to setup an LSTM in order to feed it with my numpy array features and labels.
Here is my first attempt:
nb_features =len(seq_cols)
print("initial shape:", X_train.shape)
print("nb features", nb_features)
# X_train = X_train.reshape(X_train.shape + (1,))
print("Seq length ", seq_length)
print('New shape ', X_train.shape)
model = Sequential()
model.add(LSTM(
input_shape=(nb_features, 1),
units=100,
return_sequences=True))
model.add(Dropout(0.2))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=200, verbose=1,
callbacks = [EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')])
Which gives me the output
initial shape: (175850, 4)
nb features 4
Seq length 50
New shape (175850, 4)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-99-50959413cb62> in <module>()
1 model.fit(X_train, y_train, epochs=10, batch_size=200, verbose=1,
----> 2 callbacks = [EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')])
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)
126 ': expected ' + names[i] + ' to have ' +
127 str(len(shape)) + ' dimensions, but got array '
--> 128 'with shape ' + str(data_shape))
129 if not check_batch_axis:
130 data_shape = data_shape[1:]
ValueError: Error when checking input: expected lstm_28_input to have 3 dimensions, but got array with shape (175850, 4)
So I am trying to reshape by uncommenting line 4
nb_features =len(seq_cols)
print("initial shape:", X_train.shape)
print("nb features", nb_features)
X_train = X_train.reshape(X_train.shape + (1,))
print("Seq length ", seq_length)
print('New shape ', X_train.shape)
model = Sequential()
model.add(LSTM(
input_shape=(nb_features, 1),
units=100,
return_sequences=True))
model.add(Dropout(0.2))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=200, verbose=1,
callbacks = [EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')])
which now gives me the error that I have wrong dimensions.
initial shape: (175850, 4)
nb features 4
Seq length 50
New shape (175850, 4, 1)
ValueError: Error when checking input: expected lstm_28_input to have 3 dimensions, but got array with shape (175850, 4)
I think I am just wondering around doing random changes.
Can anyone please give me an insight on what pieces I am missing from the puzzle? I am a beginner in the field and the errors are not helping me much.
P.S: X_train is a numpy array
Keras input_shape ignores the first dimension because it indicates the number of training examples, m. This is because Keras is able to work with any number of training examples, it only cares about the actual input dimensions.
For example, input_shape=(nb_features, 1)=(4,1) means it is expecting the input to be (None, 4, 1), where none is the number of training examples. You can also see this by typing model.summary() after you compile, but before you fit.
This is 3 dimensions, hence the "expected lstm_28_input to have 3 dimensions" error. You're feeding it (175850, 4) which is a two dimensional array.
I am trying to train a 2D neural network using keras. I have a weird error message, "ValueError: setting an array element with a sequence." when I try to use model.fit function in keras. Specifically, the error says that my "tensor_train_labels" is a sequence instead of an array. But my labels are indeed numpy arrays (not a sequence). I am not sure why does keras complain about it ?
I am following this tutorial for building my network
tensor_train_data.shape
#TensorShape([Dimension(209), Dimension(64), Dimension(64), Dimension(3)])
tensor_test_data.shape
#TensorShape([Dimension(50), Dimension(64), Dimension(64), Dimension(3)])
tensor_train_labels = tf.reshape(tensor_train_labels, [209,1])
tensor_test_labels = tf.reshape(tensor_test_labels, [50,1])
batch_size = 10
epochs = 8
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(32, kernel_size=(3,3), activation='relu',
input_shape=(64, 64, 3)))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))
model.add(tf.keras.layers.Dropout(0.25))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation = 'relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(2, activation = 'softmax'))
model.compile(loss='categorical_crossentropy', optimizer =
tf.keras.optimizers.Adam(lr=0.0001, decay=1e-6), metrics=['accuracy'])
model.fit(tensor_train_data/255.0,
tf.keras.utils.to_categorical(tensor_train_labels),
batch_size = batch_size,
shuffle = True,
epochs = epochs,
validation_data = (tensor_test_data/ 255.0,
tf.keras.utils.to_categorical(tensor_test_labels)))
scores = model.evaluate(tensor_test_labels/ 255.0,
tf.keras.utils.to_categorical(tensor_test_labels))
print('Loss: %.3f' % scores[0])
print('Accuracy: %.3f' % scores[1])
The Error :
ValueError Traceback (most recent call last)
<ipython-input-224-80431a1b3e79> in <module>
1 model.compile(loss='categorical_crossentropy', optimizer = tf.keras.optimizers.Adam(lr=0.0001, decay=1e-6), metrics=['accuracy'])
----> 2 model.fit(tensor_train_data/255.0, tf.keras.utils.to_categorical(tensor_train_labels),
3 batch_size = batch_size,
4 shuffle = True,
5 epochs = epochs,
~\AppData\Local\conda\conda\envs\deeplearning\lib\site-packages\tensorflow\python\keras\utils\np_utils.py in to_categorical(y,
num_classes)
37 last.
38 """
---> 39 y = np.array(y, dtype='int')
40 input_shape = y.shape
41 if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
ValueError: setting an array element with a sequence.
The possible error is that you have arrays of different sizes when you are trying to convert it into the numpy array. Possible solution : https://stackoverflow.com/a/49617425/8185479
I am getting into a similar problem with reshaping data for 1-D CNN:
I am loading data (training and testing data sets ) from a csv file with 24,325 lines. Each line is a vector of 256 numbers - independent variables plus 11 numbers of expected outcome ( labels ) [0,0,0,0,1,0,0,0,0,0,0]
I am using TensorFlow backend.
The code looks like that:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
#Importing training set
training_set = pd.read_csv("Data30.csv")
X_train = training_set.iloc[:20000, 3 :-11].values
y_train = training_set.iloc[:20000, -11:-1].values
#Importing test set
test_set = pd.read_csv("Data30.csv")
X_test = training_set.iloc[ 20001:, 3 :-11].values
y_test = training_set.iloc[ 20001:, -11:].values
X_train /= np.max(X_train) # Normalise data to [0, 1] range
X_test /= np.max(X_test) # Normalise data to [0, 1] range
print("X_train.shape[0] = " + str(X_train.shape[0]))
print("X_train.shape[1] = " + str(X_train.shape[1]))
print("y_train.shape[0] = " + str(y_train.shape[0]))
print("y_train.shape[1] = " + str(y_train.shape[1]))
print("X_test.shape[0] = " + str(X_test.shape[0]))
print("X_test.shape[1] = " + str(X_test.shape[1]))
This is what I get:
X_train.shape[0] = 20000
X_train.shape1 = 256
y_train.shape[0] = 20000
y_train.shape1 = 11
X_test.shape[0] = 4325
X_test.shape1 = 256
#Convert data into 3d tensor
# Old Version
# X_train = np.reshape(X_train,(1,X_train.shape[0],X_train.shape[1]))
# X_test = np.reshape(X_test,(1,X_test.shape[0],X_test.shape[1]))
**# New Correct Version based on the Answer:**
X_train = np.reshape(X_train,( X_train.shape[0],X_train.shape[1], 1 ))
X_test = np.reshape(X_test,( X_test.shape[0],X_test.shape[1], 1 ))
print("X_train.shape[0] = " + str(X_train.shape[0]))
print("X_train.shape[1] = " + str(X_train.shape[1]))
print("X_test.shape[0] = " + str(X_test.shape[0]))
print("X_test.shape[1] = " + str(X_test.shape[1]))
This is result of the reshaping:
X_train.shape[0] = 1
X_train.shape1 = 20000
X_test.shape[0] = 1
X_test.shape1 = 4325
#Importing convolutional layers
from keras.models import Sequential
from keras.layers import Convolution1D
from keras.layers import MaxPooling1D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers.normalization import BatchNormalization
#Initialising the CNN
classifier = Sequential()
#1.Multiple convolution and max pooling
classifier.add(Convolution1D(filters=8, kernel_size=11, activation="relu", input_shape=( 256, 1 )))
classifier.add(MaxPooling1D(strides=4))
classifier.add(BatchNormalization())
classifier.add(Convolution1D(filters=16, kernel_size=11, activation='relu'))
classifier.add(MaxPooling1D(strides=4))
classifier.add(BatchNormalization())
classifier.add(Convolution1D(filters=32, kernel_size=11, activation='relu'))
classifier.add(MaxPooling1D(strides=4))
classifier.add(BatchNormalization())
#classifier.add(Convolution1D(filters=64, kernel_size=11,activation='relu'))
#classifier.add(MaxPooling1D(strides=4))
#2.Flattening
classifier.add(Flatten())
#3.Full Connection
classifier.add(Dropout(0.5))
classifier.add(Dense(64, activation='relu'))
classifier.add(Dropout(0.25))
classifier.add(Dense(64, activation='relu'))
classifier.add(Dense(1, activation='sigmoid'))
#Configure the learning process
classifier.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
#Train!
classifier.fit_generator(training_set,
steps_per_epoch= 100,
nb_epoch = 200,
validation_data = (X_test,y_test),
validation_steps = 40)
score = classifier.evaluate(X_test, y_test)
This is the error I get:
Traceback (most recent call last):
File "C:/Conda/ML_Folder/CNN Data30.py", line 85, in
classifier.fit_generator(X_train, steps_per_epoch=10, epochs=10, validation_data=(X_test,y_test))
File "C:\Conda\lib\site-packages\keras\legacy\interfaces.py", line 87, in wrapper
return func(*args, **kwargs)
File "C:\Conda\lib\site-packages\keras\models.py", line 1121, in fit_generator
initial_epoch=initial_epoch)
File "C:\Conda\lib\site-packages\keras\legacy\interfaces.py", line 87, in wrapper
return func(*args, **kwargs)
File "C:\Conda\lib\site-packages\keras\engine\training.py", line 1978, in fit_generator
val_x, val_y, val_sample_weight)
File "C:\Conda\lib\site-packages\keras\engine\training.py", line 1378, in _standardize_user_data
exception_prefix='input')
File "C:\Conda\lib\site-packages\keras\engine\training.py", line 144, in _standardize_input_data
str(array.shape))
ValueError: Error when checking input: expected conv1d_1_input to have shape (None, 256, 1) but got array with shape (1, 4325, 256)
Can you please help me to fix the code?
Shapes should be (batchSize, length, channels)
So: (20000,256,1) and (20000,11)
Detail: your last Dense must output 11, so: Dense(11,...)
model = Sequential()
model.add(Embedding(630, 210))
model.add(LSTM(1024, dropout = 0.2, return_sequences = True))
model.add(LSTM(1024, dropout = 0.2, return_sequences = True))
model.add(Dense(210, activation = 'softmax'))
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
filepath = 'ner_2-{epoch:02d}-{loss:.5f}.hdf5'
checkpoint = ModelCheckpoint(filepath, monitor = 'loss', verbose = 1, save_best_only = True, mode = 'min')
callback_list = [checkpoint]
model.fit(X, y , epochs = 20, batch_size = 1024, callbacks = callback_list)
X: the input vector is of the shape (204564, 630, 1)
y: the target vector is of the shape (204564, 210, 1)
i.e. for every 630 inputs 210 outputs have to be predicted but the code throws the following error on compilation
ValueError Traceback (most recent call last)
<ipython-input-57-05a6affb6217> in <module>()
50 callback_list = [checkpoint]
51
---> 52 model.fit(X, y , epochs = 20, batch_size = 1024, callbacks = callback_list)
53 print('successful')
ValueError: Error when checking model input: expected embedding_8_input to have 2 dimensions, but got array with shape (204564, 630, 1)
Please someone explain why this error is occurring and how to solve this
The message says:
Your first layer expects an input with 2 dimensions: (BatchSize, SomeOtherDimension). But your input has 3 dimensions (BatchSize=204564,SomeOtherDimension=630, 1).
Well... remove the 1 from your input, or reshape it inside the model:
Solution 1 - Removing it from the input:
X = X.reshape((204564,630))
Solution 2 - Adding a reshape layer:
model = Sequential()
model.add(Reshape((630,),input_shape=(630,1)))
model.add(Embedding.....)