Trjtdtk

I am new to CNN and i want to use it for Modulation classification
I found this code and I want to replicate it as it is exept that i only used the digital modulations and some SNR (signal-Noise Ratio) levels

import os
import theano as th
import theano.tensor as T
os.environ["KERAS_BACKEND"] = "theano"
#os.environ["KERAS_BACKEND"] = "tensorflow"
os.environ["THEANO_FLAGS"] = "device=gpu%d"%(1)
import numpy as np
import keras.models as models
from keras.layers.core import Reshape,Dense,Dropout,Activation,Flatten
from keras.layers.convolutional import Convolution2D, ZeroPadding2D, Conv2D
import pickle, keras

full_dataset = pickle.load(open("RML2016.10a.pkl",'rb'),encoding='latin1')
snrs,mods = map(lambda j: sorted(list(set(map(lambda x: x[j], full_dataset.keys())))), [1,0])

digital_mods = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']
snr_levels = [-16, -12, -8, -4, 0, 4, 8, 12, 16]

X = []
lbl = []
for mod in digital_mods:
 for snr in snr_levels:
 X.append(full_dataset[(mod,snr)])
 for i in range(full_dataset[(mod,snr)].shape[0]): lbl.append((mod,snr))
X = np.vstack(X)

np.random.seed(2016)
n_examples = X.shape[0]
n_train = int(n_examples * 0.5)
train_idx = np.random.choice(range(0,n_examples), size=n_train, replace=False)
test_idx = list(set(range(0,n_examples))-set(train_idx))
X_train = X[train_idx]
X_test = X[test_idx]
def to_onehot(yy):
 yy1 = np.zeros([len(yy), max(yy)+1])
 yy1[np.arange(len(yy)),yy] = 1
 return yy1
Y_train = to_onehot(list(map(lambda x: mods.index(lbl[x][0]), train_idx)))
Y_test = to_onehot(list(map(lambda x: mods.index(lbl[x][0]), test_idx)))

in_shp = list(X_train.shape[1:])
classes = digital_mods

nb_epoch = 100 # number of epochs to train on
batch_size = 1024
dr = 0.5
model = models.Sequential()
model.add(Reshape(in_shp+[1], input_shape=in_shp))
model.add(ZeroPadding2D((0,2)))
model.add(Conv2D(64, (1,4), activation="relu"))
model.add(Dropout(dr))
model.add(ZeroPadding2D((0,2)))
model.add(Conv2D(64, (2,4), activation="relu"))
model.add(Dropout(dr))
model.add(Conv2D(128, (1,8), activation="relu"))
model.add(Dropout(dr))
model.add(Conv2D(128, (1,8), activation="relu"))
model.add(Dropout(dr))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(dr))
model.add(Dense(len(classes), activation='softmax'))
model.add(Reshape([len(classes)]))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()


filepath = 'weight_4layers.wts.h5'
history = model.fit(X_train,
 Y_train,
 batch_size=batch_size,
 nb_epoch=nb_epoch,
 verbose=1,
 validation_data=(X_test, Y_test),
 callbacks = [
 keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=True, mode='auto'),
 keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, verbose=0, mode='auto')
]) 

please note the following:

I have 8 modulation types and 9 SNR levels = 72 pairs [mod,snr]
each paire is composed of 1000 array of [2, 128] (complex values of radio signal)

X train has the shape (36000, 2, 128)
in_shape has the shape (2, 128)

So when i run my program I get the following error:

Traceback (most recent call last):
 File "/home/nechi/PycharmProjects/AMC/cnn.py", line 88, in <module>
 keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, verbose=0, mode='auto')
 File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 952, in fit
 batch_size=batch_size)
 File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 789, in _standardize_user_data
 exception_prefix='target')
 File "/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py", line 138, in standardize_input_data
 str(data_shape))
ValueError: Error when checking target: expected reshape_2 to have shape (8,) but got array with shape (10,)