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CNN model, How does dense layer in CNN divide into two streams using keras

I am working on the CNN model. In the given CNN model I can not handle how to divide the 4th layer into two streams and get output.
I also built a model in Keras.
def _build_model(self):
model = Sequential()
model.add(Conv2D(8, (3, 3), strides=4, padding='same', input_shape=self.state_size))
model.add(Activation('relu'))
model.add(Conv2D(2, (2, 2), strides=4, padding='same'))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(16, activation='relu'))
model.add(Dense(self.action_size, activation='relu'))
model.compile(loss='mse', optimizer=Adam())
return model
How to handle it? An example would be appreciated.

Keras ImageDataGenerator validation_data

Cannot really figure the error here.
Key requisites:
augmented_images and val_data_gen are keras.preprocessing.image.ImageDataGenerator functions.
model = Sequential()
model.add(Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), activation='relu', input_shape=(32,32,3)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
#model.add(Dropout(rate=0.5))
model.add(Flatten())
model.add(Dense(units=64, activation='relu'))
model.add(Dense(2, activation='sigmoid'))
model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.05),
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
model.summary()
history = model.fit(x=augmented_images,
validation_data=val_data_gen,
epochs=10,
steps_per_epoch=2000,
validation_steps=1000)
ValueError: Layer sequential_10 expects 1 inputs, but it received 5 input tensors.

LSTM layer does not accept the input shape of CNN layer output

I am trying to create a CNN + LSTM network, but the LSTM layer is not accepting the input shape. Is there anything I can do?
model = Sequential()
model.add(Conv2D(128, (2,2), padding = 'same', input_shape=(30, 216, 1)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(256, (2,2), padding = 'same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(LSTM(512, input_shape = (7, 54, 256,)))
model.add(Flatten())
model.add(Dense(7, activation='softmax'))
ValueError: Input 0 of layer lstm_21 is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, 7, 54, 256]

The LSTM layer in Keras expects this format as input:
inputs: A 3D tensor with shape [batch, timesteps, feature].
For this reason, you can't pass non-recurrent layers directly. First, Flatten() the layer before, and wrap that layer into a TimeDistributed layer, like this:
model.add(TimeDistributed(Flatten()))
model.add(LSTM(8))
This TimeDistributed layer allows to apply a layer to every temporal slice of an input. Here's a fully working example:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, LSTM, \
Dense, Flatten, Dropout, MaxPooling2D, Activation, TimeDistributed
import numpy as np
X = np.random.rand(100, 30, 216, 1)
y = np.random.randint(0, 7, 100)
model = Sequential()
model.add(Conv2D(16, (2,2), padding = 'same', input_shape=(30, 216, 1)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(32, (2,2), padding = 'same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(8))
model.add(Dense(7, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
history = model.fit(X, y)

TypeError: __init__() missing 1 required positional argument: 'units'

I am working in python and tensor flow but I miss 'units' argument and I do not know how to solve it, It looks like your post is mostly code; please add some more details.It looks like your post is mostly code; please add some more details.
here the code
def createModel():
model = Sequential()
# first set of CONV => RELU => MAX POOL layers
model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=inputShape))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))
# returns our fully constructed deep learning + Keras image classifier
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
# use binary_crossentropy if there are two classes
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
return model
print("Reshaping trainX at..."+ str(datetime.now()))
#print(trainX.sample())
print(type(trainX)) # <class 'pandas.core.series.Series'>
print(trainX.shape) # (750,)
from numpy import zeros
Xtrain = np.zeros([trainX.shape[0],HEIGHT, WIDTH, DEPTH])
for i in range(trainX.shape[0]): # 0 to traindf Size -1
Xtrain[i] = trainX[i]
print(Xtrain.shape) # (750,128,128,3)
print("Reshaped trainX at..."+ str(datetime.now()))
print("Reshaping valX at..."+ str(datetime.now()))
print(type(valX)) # <class 'pandas.core.series.Series'>
print(valX.shape) # (250,)
from numpy import zeros
Xval = np.zeros([valX.shape[0],HEIGHT, WIDTH, DEPTH])
for i in range(valX.shape[0]): # 0 to traindf Size -1
Xval[i] = valX[i]
print(Xval.shape) # (250,128,128,3)
print("Reshaped valX at..."+ str(datetime.now()))
# initialize the model
print("compiling model...")
sys.stdout.flush()
model = createModel()
# print the summary of model
from keras.utils import print_summary
print_summary(model, line_length=None, positions=None, print_fn=None)
# add some visualization
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
SVG(model_to_dot(model).create(prog='dot', format='svg'))

Try changing this line:
model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))
to
model.add(Dense(NUM_CLASSES, activation='softmax'))
I'm not experience in keras but I could not find a parameter called output_dim on the documentation page for Dense. I think you meant to provide units but labelled it as output_dim

The Keras Dense layer documentation is as follows:
keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
Using the following :
classifier.add(Dense(6, activation='relu', kernel_initializer='glorot_uniform',input_dim=11))
Will work as here the units means the output_dim saying that we need 6 neurons in the hidden layer. The weights are initialized with the uniform function and the input layer has 11 independent variables of the dataset (input_dim) to feed the above-hidden layer.

I think it's a version issue. In updated version of keras for Dense there is no "output_dim" argument.
You can see this documentation link for Dense arguments.
https://keras.io/api/layers/core_layers/dense/
tf.keras.layers.Dense(
units,
activation=None,
use_bias=True,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs
)
So the first argument is "units", Which is mandatory.
instead of this line:
model.add(Dense(output_dim=NUM_CLASSES, activation='softmax'))
use this:
model.add(Dense(units=NUM_CLASSES, activation='softmax'))
or
model.add(Dense(NUM_CLASSES, activation='softmax'))

TensorFlow Error on GPU:InvalidArgumentError: Input to reshape is a tensor with 991232 values

I have run a code from this link:
https://keras.io/getting-started/sequential-model-guide/
The code is given below:
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import SGD
with tf.device('/gpu:0'):
x_train = np.random.random((100, 100, 100, 3))
y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)
x_test = np.random.random((20, 100, 100, 3))
y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
model.fit(x_train, y_train, batch_size=32, epochs=10)
score = model.evaluate(x_test, y_test, batch_size=32)
When I run this code using CPU as with tf.device('/cpu:0'), the code runs fine completing all the epochs.
Please note my laptop support NVIDIA GeForce GT 750M.
I have installed tensorflow-gpu version 1.9.0',cuda9.2 along with NVIDIA cuDNN.
When I run the same code with GPU using tf.device('/gpu:0') its breaks even in the first epoch generating error. Any comments are appreciate spent a lot of time but could not sort it out. The error is given below:
InvalidArgumentError: Input to reshape is a tensor with 991232 values, but the requested shape requires a multiple of 1048701955
[[Node: flatten_3/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _class=["loc:#training_3/SGD/gradients/dropout_10/cond/Merge_grad/cond_grad"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](dropout_10/cond/Merge-2-0-TransposeNCHWToNHWC-LayoutOptimizer, flatten_3/stack)]]
[[Node: loss_3/mul/_283 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_790_loss_3/mul", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]