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.....)
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'm working on building a regressor. Currently the regressor will accept information through the network it appears up until the final dense layer when it throws an error. I'm new to these types of LSTM and would appreciate some help throubleshooting. I've looked through a few articles and haven't found anything that seems to work.
def generate_arrays(arrays=None, targets=None):
from random import shuffle
while True:
available_ids = list(range(len(arrays)))
shuffle(available_ids)
for i in available_ids:
frames = []
for image in arrays[i]:
# load the image
img = Image.open(image_path + image)
# convert image to numpy array
img = np.asarray(img) / 255
height = 220
width = 220
dim = (width, height)
img = cv2.resize(img, dim, interpolation=cv2.INTER_LINEAR)
# add to bigger np array
frames.append(img)
frames = np.array(frames)
frames = frames.reshape(-1, 3, 220, 220, 3)
target = targets[i]
yield (frames, category)
def build_model(frames=seq_len, channels=3, pixels_x=220, pixels_y=220):
model = Sequential()
model.add(
ConvLSTM2D(filters=10
, kernel_size=(2,2)
, data_format='channels_last'
, return_sequences = False
, activation='relu', input_shape=(frames, pixels_x, pixels_y, channels))
)
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(1, activation='linear'))
optimizer = tf.keras.optimizers.RMSprop(0.001)
model.compile(
loss = "mse",
optimizer = optimizer,
metrics=['mae', 'mse'])
return model
def evaluate_model(train_x, train_y, test_x, test_y):
# define model
model = build_model()
model.summary()
# fit network
model.fit_generator(
generate_arrays(train_x, train_y)
, steps_per_epoch = len(train_x)
, validation_data = generate_arrays(test_x, test_y)
, validation_steps = len(test_x)
, epochs = 100
, verbose = 1
, shuffle = False
, initial_epoch = 0
)
The model summary is as such:
Now, the error I'm getting appears to be the generic value error that means that something isn't getting passed correctly but I cannot figure out what it is...
ValueError: Error when checking target: expected dense_47 to have 2 dimensions, but got array with shape ()
my method has 2 input data model
network
branches:
49The first branch is composed of an embedding followed by simple Multi-layer
Perceptron (MLP) designed to handle input of the product description.
The second branch is a CNN to operate over the product image data.
These branches are then be concatenated together to form the final.
The problem is when we try to split the data with train_test_split by Cross Validation, It given as this error.
ValueError: Found input variables with inconsistent numbers of samples: [2, 8382]
MLP and CNN
def create_mlp(dim, regress=False):
# define our MLP network
model = Sequential()
model.add(Dense(8, input_dim=dim, activation="relu"))
model.add(Dense(4, activation="relu"))
# check to see if the regression node should be added
if regress:
model.add(Dense(1, activation="linear"))
# return our model
return model
def create_cnn(width, height, depth, filters=(64, 32, 16), regress=False):
# initialize the input shape and channel dimension, assuming
# TensorFlow/channels-last ordering
inputShape = (height, width, depth)
chanDim = -1
# define the model input
inputs = Input(shape=inputShape)
# loop over the number of filters
for (i, f) in enumerate(filters):
# if this is the first CONV layer then set the input
# appropriately
if i == 0:
x = inputs
# CONV => RELU => BN => POOL
x = Conv2D(f, (3, 3), padding="same")(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
# flatten the volume, then FC => RELU => BN => DROPOUT
x = Flatten()(x)
x = Dense(16)(x)
x = Activation("relu")(x)
x = BatchNormalization(axis=chanDim)(x)
x = Dropout(0.5)(x)
# apply another FC layer, this one to match the number of nodes
# coming out of the MLP
x = Dense(4)(x)
x = Activation("relu")(x)
# check to see if the regression node should be added
if regress:
x = Dense(1, activation="linear")(x)
# construct the CNN
model = Model(inputs, x)
# return the CNN
return model
mlp = create_mlp(trainEmbedX.shape[1], regress=False)
cnn = create_cnn(64, 64, 3, regress=False)
combinedInput = concatenate([mlp.output, cnn.output])
x = Dense(4, activation="relu")(combinedInput)
x = Dense(1, activation="sigmoid")(x)
model = Model(inputs=[mlp.input, cnn.input], outputs=x)
model.compile(loss="binary_crossentropy", metrics=['accuracy'], optimizer="adam") # binary_crossentropy
The error occurs here
n_folds=3
epochs=3
batch_size=128
#save the model history in a list after fitting so that we can plot later
model_history = []
for i in range(n_folds):
print("Training on Fold: ",i+1)
t_x, val_x, t_y, val_y = train_test_split([trainEmbedX,trainImagesX], trainY, test_size = 0.2, random_state = np.random.randint(1,1000, 1)[0])
model_history.append(fit_and_evaluate(t_x, val_x, t_y, val_y, epochs, batch_size))
print("======="*12, end="\n\n\n")
Training on Fold: 1
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-84-651638774259> in <module>
7 for i in range(n_folds):
8 print("Training on Fold: ",i+1)
----> 9 t_x, val_x, t_y, val_y = train_test_split([trainEmbedX,trainImagesX], trainY, test_size = 0.2, random_state = np.random.randint(1,1000, 1)[0])
10 model_history.append(fit_and_evaluate(t_x, val_x, t_y, val_y, epochs, batch_size))
11 print("======="*12, end="\n\n\n")
~/anaconda3/envs/baron/lib/python3.6/site-packages/sklearn/model_selection/_split.py in train_test_split(*arrays, **options)
2182 test_size = 0.25
2183
-> 2184 arrays = indexable(*arrays)
2185
2186 if shuffle is False:
~/anaconda3/envs/baron/lib/python3.6/site-packages/sklearn/utils/validation.py in indexable(*iterables)
258 else:
259 result.append(np.array(X))
--> 260 check_consistent_length(*result)
261 return result
262
~/anaconda3/envs/baron/lib/python3.6/site-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
233 if len(uniques) > 1:
234 raise ValueError("Found input variables with inconsistent numbers of"
--> 235 " samples: %r" % [int(l) for l in lengths])
236
237
ValueError: Found input variables with inconsistent numbers of samples: [2, 8382]
This error happens with mismatching dimensions of X and Y in train_test_split.
By looking at your snippet, you try to concatenate two arrays by [trainEmbedX,trainImagesX] which will add a dimension if the original arrays trainEmbedX and trainImagesX are not 1D, hence you have the shape [2, 8382] in the error.
So instead of [trainEmbedX,trainImagesX], I suggest to use np.concatenate to merge these two arrays by np.concatenate((trainEmbedX,trainImagesX),axis=1).
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
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